Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Discovery Science [[electronic resource] ] : 20th International Conference, DS 2017, Kyoto, Japan, October 15–17, 2017, Proceedings / / edited by Akihiro Yamamoto, Takuya Kida, Takeaki Uno, Tetsuji Kuboyama
| Discovery Science [[electronic resource] ] : 20th International Conference, DS 2017, Kyoto, Japan, October 15–17, 2017, Proceedings / / edited by Akihiro Yamamoto, Takuya Kida, Takeaki Uno, Tetsuji Kuboyama |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XV, 357 p. 90 illus.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Application software Artificial Intelligence Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-319-67786-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Machine Learning from Weak Supervision - Towards Accurate Classification with Low Labeling Costs -- Automatic Design of Functional Molecules and Materials -- Contents -- Online Learning -- Context-Based Abrupt Change Detection and Adaptation for Categorical Data Streams -- 1 Introduction -- 2 Background -- 2.1 DILCA Context-Based Similarity Measure -- 2.2 CDCStream Categorical Drift Detector -- 3 FG-CDCStream Algorithm -- 4 Experimentation -- 4.1 Results -- 4.2 Discussion -- 5 Conclusion -- References -- A New Adaptive Learning Algorithm and Its Application to Online Malware Detection -- 1 Introduction -- 2 Related Work -- 2.1 Batch Learning in Malware Detection -- 2.2 Online Learning in Malware Detection -- 3 Problem Statement -- 4 Methodology -- 4.1 Batch Learning -- LR -- 4.2 Online Learning -- FTRL-DP -- 5 Data Collection -- 5.1 Malware Collection -- 5.2 Malware Execution -- 5.3 Feature Extraction -- 6 Evaluation -- 6.1 Experiment with LR -- 6.2 Experiment with FTRL Algorithms -- 7 Discussion -- 7.1 Prediction Accuracy -- 7.2 Running Time -- 8 Conclusions and Future Work -- A Proof of Theorem1 -- References -- Real-Time Validation of Retail Gasoline Prices -- 1 Introduction -- 2 Related Research -- 2.1 Factors Affecting Gasoline Prices -- 2.2 The Most Common Action Model -- 3 The PCR Real-Time Prediction Model -- 3.1 Price Change Rules -- 3.2 Prediction Using Price Change Rules -- 3.3 Description of the Method -- 3.4 Evaluation of the PCR Method -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Regression -- General Meta-Model Framework for Surrogate-Based Numerical Optimization -- 1 Introduction -- 2 Background and Related Work -- 2.1 Numerical Optimization -- 2.2 Estimating Parameters of Ordinary Differential Equations -- 2.3 Surrogate-Based Numerical Optimization.
3 Meta Model for Surrogate-Based Optimization -- 3.1 Meta-Model Framework -- 3.2 Uninformed Meta Model -- 3.3 Relevator Meta Model -- 4 Empirical Evaluation of the Meta-Model Variants -- 4.1 Parameter Estimation Problems -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression -- 1 Introduction -- 2 Problem Formulation -- 3 Related Work -- 4 Empirical Approaches Based on Prediction Shift -- 4.1 Constant Shift -- 4.2 Pointwise Shift -- 4.3 Learned Model-Based Shift -- 4.4 Assumed Error Model-Based Shift -- 4.5 k-Nearest Neighbors Based Methods -- 5 Experimental Comparison of Methods -- 5.1 Regression Methods Used -- 5.2 Loss Functions Used -- 5.3 Results -- 6 Conclusion -- References -- Differentially Private Empirical Risk Minimization with Input Perturbation -- 1 Introduction -- 2 Problem Definition and Preliminary -- 3 Input Perturbation -- 3.1 Loss Function for Input Perturbation -- 3.2 Input Perturbation Method -- 3.3 Privacy of Input Perturbation -- 3.4 Utility Analysis -- 4 Conclusion -- References -- Label Classification -- On a New Competence Measure Applied to the Dynamic Selection of Classifiers Ensemble -- 1 Introduction -- 2 Multiclassifier System -- 2.1 Preliminaries -- 2.2 Measure of Competence -- 2.3 DES Systems -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- Multi-label Classification Using Random Label Subset Selections -- 1 Introduction -- 2 MLC Using Random Label Subset Selections -- 3 Experimental Design -- 4 Results -- 5 Conclusions and Future Work -- References -- Option Predictive Clustering Trees for Hierarchical Multi-label Classification -- 1 Introduction -- 2 Option Predictive Clustering Trees -- 3 Experimental Design -- 4 Results and Discussion. 5 Conclusions -- References -- Deep Learning -- Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction -- 1 Introduction -- 2 Knowledge Distillation from Neural Networks -- 2.1 Rule Extraction -- 2.2 Connection Pruning -- 3 The DEEPRED Algorithm -- 3.1 Overview -- 3.2 Extraction of DNF Formulas from Trees -- 3.3 Simplification and Post-pruning of Expressions -- 4 Retraining DNNs to Extract Better Representations -- 4.1 Weight Sparseness Pruning -- 4.2 Activation Polarization -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Characteristics of the Trained Networks -- 5.3 Reconstruction Using the Entire Dataset -- 5.4 Reconstruction Using Part of the Dataset -- 6 Conclusion -- References -- An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modeling -- 1 Introduction -- 2 Method -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Task 1: Sentiment Analysis -- 3.3 Task 2: Sentence Categorization -- 3.4 Comparison of CNN Architectures -- 4 Conclusions -- References -- Feature Selection -- Improving Classification Accuracy by Means of the Sliding Window Method in Consistency-Based Feature Selection -- 1 Introduction -- 2 Feature Selection Methods -- 2.1 Feature Ranking Methods -- 2.2 Pairwise Evaluation Methods -- 2.3 Consistency-Based Algorithms -- 3 Our Proposal -- 3.1 Defieciencies of steepest-descent Search -- 4 Experiments -- 4.1 Numbers of Features Selected and Auc-Roc Scores -- 4.2 Efficiency -- 5 Conclusion and Future Works -- References -- Feature Ranking for Multi-target Regression with Tree Ensemble Methods -- 1 Introduction -- 2 Predictive Clustering Trees for Multi-target Regression -- 3 Feature Ranking via Ensembles of PCTs -- 3.1 Ensembles of PCTs -- 3.2 Ensemble Feature Ranking Methods -- 4 Experimental Design -- 4.1 Experimental Questions -- 4.2 Data Description -- 4.3 Evaluation Methodology. 4.4 Statistical Analysis of the Results -- 4.5 Parameter Instantiation -- 5 Results and Discussion -- 5.1 Are the Obtained Feature Rankings Relevant? -- 5.2 Comparison of the Different Ranking Methods -- 5.3 Comparison of the Different Ensemble Methods -- 5.4 Selecting the Best Ensemble-Ranking Pair -- 6 Conclusions -- References -- Recommendation System -- Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering -- 2.2 Metalearning -- 2.3 Algorithm Selection for CF -- 3 Subsampling Landmarkers for Collaborative Filtering -- 3.1 Subsampling Landmarkers -- 3.2 Experimental Procedure -- 4 Results and Discussion -- 4.1 Metalevel Evaluation -- 4.2 Baselevel Performance Analysis -- 4.3 Metaknowledge -- 5 Conclusions and Future Work -- References -- Community Detection -- Recursive Extraction of Modular Structure from Layered Neural Networks Using Variational Bayes Method -- 1 Introduction -- 2 Layered Neural Networks -- 3 Recursive Extraction of Modular Structure from Layered Neural Networks Using Variational Bayes Method -- 3.1 Community Detection Using Variational Bayes Method -- 3.2 Modular Representation of Layered Neural Networks -- 3.3 Recursive Extraction of Modular Structure and Pruning -- 4 Experiment -- 4.1 Discovery of a Hidden Structure in a Layered Neural Network -- 4.2 Analysis of STUDENT ALCOHOL CONSUMPTION Data Set -- 5 Discussion -- 6 Conclusion -- References -- Discovering Hidden Knowledge in Carbon Emissions Data: A Multilayer Network Approach -- 1 Introduction -- 2 Data Collection and Preparation -- 3 Proposed Multilayer Network -- 3.1 Building a Multilayer Network -- 3.2 Community Detection on Multilayer Networks -- 4 Results and Discussion -- 4.1 Knowledge Discovery in Carbon Emissions Networks -- 4.2 Community Detection on CETA-MLN. 4.3 Impact of Varying Coupling and Resolution Parameters -- 5 Conclusion and Future Work -- References -- Topic Extraction on Twitter Considering Author's Role Based on Bipartite Networks -- 1 Introduction -- 2 Related Work -- 3 Community Detection Technique from Bipartite Networks -- 4 Proposed Method -- 4.1 Step 1: Creation of Author-Word Count Matrices -- 4.2 Step 2: Clustering and Step 3: Feature Selection -- 4.3 Step 4: Topic Detection -- 5 Experimental Result -- 5.1 Target Data -- 5.2 Step 1: Creation of Author-Word Count Matrices -- 5.3 Step 2: Clustering and Step 3: Feature Selection -- 5.4 Step 4: Topic Detection -- 6 Conclusion -- References -- Pattern Mining -- Mining Strongly Closed Itemsets from Data Streams -- 1 Introduction -- 2 Related Work -- 3 The Problem Setting -- 4 The Mining Algorithm -- 4.1 Sampling -- 4.2 Incremental Update -- 5 Empirical Evaluation -- 5.1 Speedup -- 5.2 Approximation Quality -- 6 Concluding Remarks -- References -- Extracting Mutually Dependent Multisets -- 1 Introduction -- 2 Mutually Dependent Multisets -- 3 Tail Occurrence of Multisets -- 4 Algorithm to Extract Mutually Dependent Multisets -- 5 Experimental Results -- 5.1 Antibiogram -- 5.2 Artificial Data -- 6 Conclusion and Future Works -- References -- Bioinformatics -- LOCANDA: Exploiting Causality in the Reconstruction of Gene Regulatory Networks -- 1 Introduction -- 2 Related Work -- 3 The Method LOCANDA -- 4 Experiments -- 5 Conclusions and Future Work -- References -- Discovery of Salivary Gland Tumors' Biomarkers via Co-Regularized Sparse-Group Lasso -- 1 Background and Motivation -- 2 Previous Work -- 3 Methodology -- 3.1 Co-Regularized Sparse-Group Lasso -- 3.2 Stability Selection and Randomization Test -- 4 Experiments -- 5 Results and Discussion -- 5.1 Model Performance Evaluation -- 5.2 Biological Interpretation -- 6 Conclusions -- References. Knowledge Discovery. |
| Record Nr. | UNISA-996466246103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Discovery Science : 20th International Conference, DS 2017, Kyoto, Japan, October 15–17, 2017, Proceedings / / edited by Akihiro Yamamoto, Takuya Kida, Takeaki Uno, Tetsuji Kuboyama
| Discovery Science : 20th International Conference, DS 2017, Kyoto, Japan, October 15–17, 2017, Proceedings / / edited by Akihiro Yamamoto, Takuya Kida, Takeaki Uno, Tetsuji Kuboyama |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XV, 357 p. 90 illus.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Application software Artificial Intelligence Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-319-67786-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Machine Learning from Weak Supervision - Towards Accurate Classification with Low Labeling Costs -- Automatic Design of Functional Molecules and Materials -- Contents -- Online Learning -- Context-Based Abrupt Change Detection and Adaptation for Categorical Data Streams -- 1 Introduction -- 2 Background -- 2.1 DILCA Context-Based Similarity Measure -- 2.2 CDCStream Categorical Drift Detector -- 3 FG-CDCStream Algorithm -- 4 Experimentation -- 4.1 Results -- 4.2 Discussion -- 5 Conclusion -- References -- A New Adaptive Learning Algorithm and Its Application to Online Malware Detection -- 1 Introduction -- 2 Related Work -- 2.1 Batch Learning in Malware Detection -- 2.2 Online Learning in Malware Detection -- 3 Problem Statement -- 4 Methodology -- 4.1 Batch Learning -- LR -- 4.2 Online Learning -- FTRL-DP -- 5 Data Collection -- 5.1 Malware Collection -- 5.2 Malware Execution -- 5.3 Feature Extraction -- 6 Evaluation -- 6.1 Experiment with LR -- 6.2 Experiment with FTRL Algorithms -- 7 Discussion -- 7.1 Prediction Accuracy -- 7.2 Running Time -- 8 Conclusions and Future Work -- A Proof of Theorem1 -- References -- Real-Time Validation of Retail Gasoline Prices -- 1 Introduction -- 2 Related Research -- 2.1 Factors Affecting Gasoline Prices -- 2.2 The Most Common Action Model -- 3 The PCR Real-Time Prediction Model -- 3.1 Price Change Rules -- 3.2 Prediction Using Price Change Rules -- 3.3 Description of the Method -- 3.4 Evaluation of the PCR Method -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Regression -- General Meta-Model Framework for Surrogate-Based Numerical Optimization -- 1 Introduction -- 2 Background and Related Work -- 2.1 Numerical Optimization -- 2.2 Estimating Parameters of Ordinary Differential Equations -- 2.3 Surrogate-Based Numerical Optimization.
3 Meta Model for Surrogate-Based Optimization -- 3.1 Meta-Model Framework -- 3.2 Uninformed Meta Model -- 3.3 Relevator Meta Model -- 4 Empirical Evaluation of the Meta-Model Variants -- 4.1 Parameter Estimation Problems -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression -- 1 Introduction -- 2 Problem Formulation -- 3 Related Work -- 4 Empirical Approaches Based on Prediction Shift -- 4.1 Constant Shift -- 4.2 Pointwise Shift -- 4.3 Learned Model-Based Shift -- 4.4 Assumed Error Model-Based Shift -- 4.5 k-Nearest Neighbors Based Methods -- 5 Experimental Comparison of Methods -- 5.1 Regression Methods Used -- 5.2 Loss Functions Used -- 5.3 Results -- 6 Conclusion -- References -- Differentially Private Empirical Risk Minimization with Input Perturbation -- 1 Introduction -- 2 Problem Definition and Preliminary -- 3 Input Perturbation -- 3.1 Loss Function for Input Perturbation -- 3.2 Input Perturbation Method -- 3.3 Privacy of Input Perturbation -- 3.4 Utility Analysis -- 4 Conclusion -- References -- Label Classification -- On a New Competence Measure Applied to the Dynamic Selection of Classifiers Ensemble -- 1 Introduction -- 2 Multiclassifier System -- 2.1 Preliminaries -- 2.2 Measure of Competence -- 2.3 DES Systems -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- Multi-label Classification Using Random Label Subset Selections -- 1 Introduction -- 2 MLC Using Random Label Subset Selections -- 3 Experimental Design -- 4 Results -- 5 Conclusions and Future Work -- References -- Option Predictive Clustering Trees for Hierarchical Multi-label Classification -- 1 Introduction -- 2 Option Predictive Clustering Trees -- 3 Experimental Design -- 4 Results and Discussion. 5 Conclusions -- References -- Deep Learning -- Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction -- 1 Introduction -- 2 Knowledge Distillation from Neural Networks -- 2.1 Rule Extraction -- 2.2 Connection Pruning -- 3 The DEEPRED Algorithm -- 3.1 Overview -- 3.2 Extraction of DNF Formulas from Trees -- 3.3 Simplification and Post-pruning of Expressions -- 4 Retraining DNNs to Extract Better Representations -- 4.1 Weight Sparseness Pruning -- 4.2 Activation Polarization -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Characteristics of the Trained Networks -- 5.3 Reconstruction Using the Entire Dataset -- 5.4 Reconstruction Using Part of the Dataset -- 6 Conclusion -- References -- An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modeling -- 1 Introduction -- 2 Method -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Task 1: Sentiment Analysis -- 3.3 Task 2: Sentence Categorization -- 3.4 Comparison of CNN Architectures -- 4 Conclusions -- References -- Feature Selection -- Improving Classification Accuracy by Means of the Sliding Window Method in Consistency-Based Feature Selection -- 1 Introduction -- 2 Feature Selection Methods -- 2.1 Feature Ranking Methods -- 2.2 Pairwise Evaluation Methods -- 2.3 Consistency-Based Algorithms -- 3 Our Proposal -- 3.1 Defieciencies of steepest-descent Search -- 4 Experiments -- 4.1 Numbers of Features Selected and Auc-Roc Scores -- 4.2 Efficiency -- 5 Conclusion and Future Works -- References -- Feature Ranking for Multi-target Regression with Tree Ensemble Methods -- 1 Introduction -- 2 Predictive Clustering Trees for Multi-target Regression -- 3 Feature Ranking via Ensembles of PCTs -- 3.1 Ensembles of PCTs -- 3.2 Ensemble Feature Ranking Methods -- 4 Experimental Design -- 4.1 Experimental Questions -- 4.2 Data Description -- 4.3 Evaluation Methodology. 4.4 Statistical Analysis of the Results -- 4.5 Parameter Instantiation -- 5 Results and Discussion -- 5.1 Are the Obtained Feature Rankings Relevant? -- 5.2 Comparison of the Different Ranking Methods -- 5.3 Comparison of the Different Ensemble Methods -- 5.4 Selecting the Best Ensemble-Ranking Pair -- 6 Conclusions -- References -- Recommendation System -- Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering -- 2.2 Metalearning -- 2.3 Algorithm Selection for CF -- 3 Subsampling Landmarkers for Collaborative Filtering -- 3.1 Subsampling Landmarkers -- 3.2 Experimental Procedure -- 4 Results and Discussion -- 4.1 Metalevel Evaluation -- 4.2 Baselevel Performance Analysis -- 4.3 Metaknowledge -- 5 Conclusions and Future Work -- References -- Community Detection -- Recursive Extraction of Modular Structure from Layered Neural Networks Using Variational Bayes Method -- 1 Introduction -- 2 Layered Neural Networks -- 3 Recursive Extraction of Modular Structure from Layered Neural Networks Using Variational Bayes Method -- 3.1 Community Detection Using Variational Bayes Method -- 3.2 Modular Representation of Layered Neural Networks -- 3.3 Recursive Extraction of Modular Structure and Pruning -- 4 Experiment -- 4.1 Discovery of a Hidden Structure in a Layered Neural Network -- 4.2 Analysis of STUDENT ALCOHOL CONSUMPTION Data Set -- 5 Discussion -- 6 Conclusion -- References -- Discovering Hidden Knowledge in Carbon Emissions Data: A Multilayer Network Approach -- 1 Introduction -- 2 Data Collection and Preparation -- 3 Proposed Multilayer Network -- 3.1 Building a Multilayer Network -- 3.2 Community Detection on Multilayer Networks -- 4 Results and Discussion -- 4.1 Knowledge Discovery in Carbon Emissions Networks -- 4.2 Community Detection on CETA-MLN. 4.3 Impact of Varying Coupling and Resolution Parameters -- 5 Conclusion and Future Work -- References -- Topic Extraction on Twitter Considering Author's Role Based on Bipartite Networks -- 1 Introduction -- 2 Related Work -- 3 Community Detection Technique from Bipartite Networks -- 4 Proposed Method -- 4.1 Step 1: Creation of Author-Word Count Matrices -- 4.2 Step 2: Clustering and Step 3: Feature Selection -- 4.3 Step 4: Topic Detection -- 5 Experimental Result -- 5.1 Target Data -- 5.2 Step 1: Creation of Author-Word Count Matrices -- 5.3 Step 2: Clustering and Step 3: Feature Selection -- 5.4 Step 4: Topic Detection -- 6 Conclusion -- References -- Pattern Mining -- Mining Strongly Closed Itemsets from Data Streams -- 1 Introduction -- 2 Related Work -- 3 The Problem Setting -- 4 The Mining Algorithm -- 4.1 Sampling -- 4.2 Incremental Update -- 5 Empirical Evaluation -- 5.1 Speedup -- 5.2 Approximation Quality -- 6 Concluding Remarks -- References -- Extracting Mutually Dependent Multisets -- 1 Introduction -- 2 Mutually Dependent Multisets -- 3 Tail Occurrence of Multisets -- 4 Algorithm to Extract Mutually Dependent Multisets -- 5 Experimental Results -- 5.1 Antibiogram -- 5.2 Artificial Data -- 6 Conclusion and Future Works -- References -- Bioinformatics -- LOCANDA: Exploiting Causality in the Reconstruction of Gene Regulatory Networks -- 1 Introduction -- 2 Related Work -- 3 The Method LOCANDA -- 4 Experiments -- 5 Conclusions and Future Work -- References -- Discovery of Salivary Gland Tumors' Biomarkers via Co-Regularized Sparse-Group Lasso -- 1 Background and Motivation -- 2 Previous Work -- 3 Methodology -- 3.1 Co-Regularized Sparse-Group Lasso -- 3.2 Stability Selection and Randomization Test -- 4 Experiments -- 5 Results and Discussion -- 5.1 Model Performance Evaluation -- 5.2 Biological Interpretation -- 6 Conclusions -- References. Knowledge Discovery. |
| Record Nr. | UNINA-9910484792603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discovery Science [[electronic resource] ] : 6th International Conference, DS 2003, Sapporo, Japan, October 17-19,2003, Proceedings / / edited by Gunter Grieser, Yuzuru Tanaka, Akihiro Yamamoto
| Discovery Science [[electronic resource] ] : 6th International Conference, DS 2003, Sapporo, Japan, October 17-19,2003, Proceedings / / edited by Gunter Grieser, Yuzuru Tanaka, Akihiro Yamamoto |
| Edizione | [1st ed. 2003.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
| Descrizione fisica | 1 online resource (XII, 504 p.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Science—Philosophy
Artificial intelligence Database management Information storage and retrieval systems Information technology—Management Business information services Philosophy of Science Artificial Intelligence Database Management Information Storage and Retrieval Computer Application in Administrative Data Processing IT in Business |
| ISBN | 3-540-39644-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Abduction and the Dualization Problem -- Signal Extraction and Knowledge Discovery Based on Statistical Modeling -- Association Computation for Information Access -- Efficient Data Representations That Preserve Information -- Can Learning in the Limit Be Done Efficiently? -- Long Papers -- Discovering Frequent Substructures in Large Unordered Trees -- Discovering Rich Navigation Patterns on a Web Site -- Mining Frequent Itemsets with Category-Based Constraints -- Modelling Soil Radon Concentration for Earthquake Prediction -- Dialectical Evidence Assembly for Discovery -- Performance Analysis of a Greedy Algorithm for Inferring Boolean Functions -- Performance Evaluation of Decision Tree Graph-Based Induction -- Discovering Ecosystem Models from Time-Series Data -- An Optimal Strategy for Extracting Probabilistic Rules by Combining Rough Sets and Genetic Algorithm -- Extraction of Coverings as Monotone DNF Formulas -- What Kinds and Amounts of Causal Knowledge Can Be Acquired from Text by Using Connective Markers as Clues? -- Clustering Orders -- Business Application for Sales Transaction Data by Using Genome Analysis Technology -- Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates -- Chaining Patterns -- An Algorithm for Discovery of New Families of Optimal Regular Networks -- Enumerating Maximal Frequent Sets Using Irredundant Dualization -- Discovering Exceptional Information from Customer Inquiry by Association Rule Miner -- Short Papers -- Automatic Classification for the Identification of Relationships in a Meta-Data Repository -- Effects of Unreliable Group Profiling by Means of Data Mining -- Using Constraints in Discovering Dynamics -- SA-Optimized Multiple View Smooth Polyhedron Representation NN -- Elements of an Agile Discovery Environment -- Discovery of User Preference in Personalized Design Recommender System through Combining Collaborative Filtering and Content Based Filtering -- Discovery of Relationships between Interests from Bulletin Board System by Dissimilarity Reconstruction -- A Genetic Algorithm for Inferring Pseudoknotted RNA Structures from Sequence Data -- Prediction of Molecular Bioactivity for Drug Design Using a Decision Tree Algorithm -- Mining RNA Structure Elements from the Structure Data of Protein-RNA Complexes -- Discovery of Cellular Automata Rules Using Cases -- Discovery of Web Communities from Positive and Negative Examples -- Association Rules and Dempster-Shafer Theory of Evidence -- Subgroup Discovery among Personal Homepages -- Collaborative Filtering Using Projective Restoration Operators -- Discovering Homographs Using N-Partite Graph Clustering -- Discovery of Trends and States in Irregular Medical Temporal Data -- Creating Abstract Concepts for Classification by Finding Top-N Maximal Weighted Cliques -- Content-Based Scene Change Detection of Video Sequence Using Hierarchical Hidden Markov Model -- An Appraisal of UNIVAUTO – The First Discovery Program to Generate a Scientific Article -- Scilog: A Language for Scientific Processes and Scales -- Mining Multiple Clustering Data for Knowledge Discovery -- Bacterium Lingualis – The Web-Based Commonsensical Knowledge Discovery Method -- Inducing Biological Models from Temporal Gene Expression Data -- Knowledge Discovery on Chemical Reactivity from Experimental Reaction Information -- A Method of Extracting Related Words Using Standardized Mutual Information -- Discovering Most Classificatory Patterns for Very Expressive Pattern Classes -- Mining Interesting Patterns Using Estimated Frequencies from Subpatterns and Superpatterns. |
| Record Nr. | UNISA-996465798903316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Discovery Science : 6th International Conference, DS 2003, Sapporo, Japan, October 17-19,2003, Proceedings / / edited by Gunter Grieser, Yuzuru Tanaka, Akihiro Yamamoto
| Discovery Science : 6th International Conference, DS 2003, Sapporo, Japan, October 17-19,2003, Proceedings / / edited by Gunter Grieser, Yuzuru Tanaka, Akihiro Yamamoto |
| Edizione | [1st ed. 2003.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
| Descrizione fisica | 1 online resource (XII, 504 p.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Science—Philosophy
Artificial intelligence Database management Information storage and retrieval systems Information technology—Management Business information services Philosophy of Science Artificial Intelligence Database Management Information Storage and Retrieval Computer Application in Administrative Data Processing IT in Business |
| ISBN | 3-540-39644-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Abduction and the Dualization Problem -- Signal Extraction and Knowledge Discovery Based on Statistical Modeling -- Association Computation for Information Access -- Efficient Data Representations That Preserve Information -- Can Learning in the Limit Be Done Efficiently? -- Long Papers -- Discovering Frequent Substructures in Large Unordered Trees -- Discovering Rich Navigation Patterns on a Web Site -- Mining Frequent Itemsets with Category-Based Constraints -- Modelling Soil Radon Concentration for Earthquake Prediction -- Dialectical Evidence Assembly for Discovery -- Performance Analysis of a Greedy Algorithm for Inferring Boolean Functions -- Performance Evaluation of Decision Tree Graph-Based Induction -- Discovering Ecosystem Models from Time-Series Data -- An Optimal Strategy for Extracting Probabilistic Rules by Combining Rough Sets and Genetic Algorithm -- Extraction of Coverings as Monotone DNF Formulas -- What Kinds and Amounts of Causal Knowledge Can Be Acquired from Text by Using Connective Markers as Clues? -- Clustering Orders -- Business Application for Sales Transaction Data by Using Genome Analysis Technology -- Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates -- Chaining Patterns -- An Algorithm for Discovery of New Families of Optimal Regular Networks -- Enumerating Maximal Frequent Sets Using Irredundant Dualization -- Discovering Exceptional Information from Customer Inquiry by Association Rule Miner -- Short Papers -- Automatic Classification for the Identification of Relationships in a Meta-Data Repository -- Effects of Unreliable Group Profiling by Means of Data Mining -- Using Constraints in Discovering Dynamics -- SA-Optimized Multiple View Smooth Polyhedron Representation NN -- Elements of an Agile Discovery Environment -- Discovery of User Preference in Personalized Design Recommender System through Combining Collaborative Filtering and Content Based Filtering -- Discovery of Relationships between Interests from Bulletin Board System by Dissimilarity Reconstruction -- A Genetic Algorithm for Inferring Pseudoknotted RNA Structures from Sequence Data -- Prediction of Molecular Bioactivity for Drug Design Using a Decision Tree Algorithm -- Mining RNA Structure Elements from the Structure Data of Protein-RNA Complexes -- Discovery of Cellular Automata Rules Using Cases -- Discovery of Web Communities from Positive and Negative Examples -- Association Rules and Dempster-Shafer Theory of Evidence -- Subgroup Discovery among Personal Homepages -- Collaborative Filtering Using Projective Restoration Operators -- Discovering Homographs Using N-Partite Graph Clustering -- Discovery of Trends and States in Irregular Medical Temporal Data -- Creating Abstract Concepts for Classification by Finding Top-N Maximal Weighted Cliques -- Content-Based Scene Change Detection of Video Sequence Using Hierarchical Hidden Markov Model -- An Appraisal of UNIVAUTO – The First Discovery Program to Generate a Scientific Article -- Scilog: A Language for Scientific Processes and Scales -- Mining Multiple Clustering Data for Knowledge Discovery -- Bacterium Lingualis – The Web-Based Commonsensical Knowledge Discovery Method -- Inducing Biological Models from Temporal Gene Expression Data -- Knowledge Discovery on Chemical Reactivity from Experimental Reaction Information -- A Method of Extracting Related Words Using Standardized Mutual Information -- Discovering Most Classificatory Patterns for Very Expressive Pattern Classes -- Mining Interesting Patterns Using Estimated Frequencies from Subpatterns and Superpatterns. |
| Record Nr. | UNINA-9910768438603321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
| Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 215 p. 56 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Data mining Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Data Mining and Knowledge Discovery |
| ISBN | 3-319-40566-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter Learning for Probabilistic Ontologies -- 1 Introduction -- 2 Description Logics -- 3 Semantics and Reasoning in Probabilistic DLs -- 4 Parameter Learning for Probabilistic DLs -- 5 Distributed Parameter Learning for Probabilistic DLs -- 5.1 Architecture -- 5.2 MapReduce View -- 5.3 Scheduling Techniques -- 5.4 Overall EDGEMR -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Meta-Interpretive Learning of Data Transformation Programs -- 1 Introduction -- 2 Related Work -- 3 Framework -- 4 Implementation -- 4.1 Transformation Language -- 5 Experiments -- 5.1 XML Data Transformations -- 5.2 Ecological Scholarly Papers -- 5.3 Patient Medical Records -- 6 Conclusion and Further Work -- A Appendix 1 -- B Appendix 2 -- References -- Statistical Relational Learning with Soft Quantifiers -- 1 Introduction -- 2 PSLQ: PSL with Soft Quantifiers -- 3 Inference and Weight Learning in PSLQ -- 3.1 Inference -- 3.2 Weight Learning -- 4 Evaluation: Trust Link Prediction -- 5 Conclusion -- References.
Ontology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method. 4.3 Results for Warmr/TreeLiker Combined Method -- 5 Conclusions -- References -- kProbLog: An Algebraic Prolog for Kernel Programming -- 1 Introduction -- 2 KProbLogS -- 3 kProbLog -- 3.1 Recursive kProbLog Program with Meta-Functions -- 3.2 The Jacobi Method -- 3.3 kProbLog TP-Operator with Meta-Functions -- 4 kProbLogS[x] -- 4.1 Polynomials for Feature Extraction -- 4.2 The @id Meta-Function -- 5 Graph Kernels -- 5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels -- 5.2 Graph Invariant Kernels -- 6 Conclusions -- References -- An Exercise in Declarative Modeling for Relational Query Mining -- 1 Introduction -- 2 Problem Statement -- 3 Encoding -- 4 First Order Model -- 5 Experiments -- 6 Model Discussion and Generalization -- 7 Related Work -- 8 Conclusions -- A Appendix: Introduction to IDP -- References -- Learning Inference by Induction -- 1 Introduction -- 2 Learning Logical Inference -- 2.1 Learning Logics -- 2.2 Learning from 1-Step Transitions -- 2.3 Learning Deduction Rules by LF1T -- 3 Learning Non-logical Inference Rules -- 3.1 Abduction -- 3.2 Frame Axiom -- 3.3 Conversational Implicature -- 4 Discussion -- 5 Conclusion -- References -- Identification of Transition Models of Biological Systems in the Presence of Transition Noise -- 1 Introduction -- 2 Transition Identification Under Transition Noise -- 3 Empirical Evaluation -- 3.1 Problems -- 3.2 Data -- 3.3 Models -- 3.4 Algorithms and Machines -- 3.5 Method -- 3.6 Results -- 3.7 Transition Identification Worked Example: Water -- 4 Related Work -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996466006103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive Logic Programming : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
| Inductive Logic Programming : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 215 p. 56 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Data mining Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Data Mining and Knowledge Discovery |
| ISBN | 3-319-40566-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter Learning for Probabilistic Ontologies -- 1 Introduction -- 2 Description Logics -- 3 Semantics and Reasoning in Probabilistic DLs -- 4 Parameter Learning for Probabilistic DLs -- 5 Distributed Parameter Learning for Probabilistic DLs -- 5.1 Architecture -- 5.2 MapReduce View -- 5.3 Scheduling Techniques -- 5.4 Overall EDGEMR -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Meta-Interpretive Learning of Data Transformation Programs -- 1 Introduction -- 2 Related Work -- 3 Framework -- 4 Implementation -- 4.1 Transformation Language -- 5 Experiments -- 5.1 XML Data Transformations -- 5.2 Ecological Scholarly Papers -- 5.3 Patient Medical Records -- 6 Conclusion and Further Work -- A Appendix 1 -- B Appendix 2 -- References -- Statistical Relational Learning with Soft Quantifiers -- 1 Introduction -- 2 PSLQ: PSL with Soft Quantifiers -- 3 Inference and Weight Learning in PSLQ -- 3.1 Inference -- 3.2 Weight Learning -- 4 Evaluation: Trust Link Prediction -- 5 Conclusion -- References.
Ontology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method. 4.3 Results for Warmr/TreeLiker Combined Method -- 5 Conclusions -- References -- kProbLog: An Algebraic Prolog for Kernel Programming -- 1 Introduction -- 2 KProbLogS -- 3 kProbLog -- 3.1 Recursive kProbLog Program with Meta-Functions -- 3.2 The Jacobi Method -- 3.3 kProbLog TP-Operator with Meta-Functions -- 4 kProbLogS[x] -- 4.1 Polynomials for Feature Extraction -- 4.2 The @id Meta-Function -- 5 Graph Kernels -- 5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels -- 5.2 Graph Invariant Kernels -- 6 Conclusions -- References -- An Exercise in Declarative Modeling for Relational Query Mining -- 1 Introduction -- 2 Problem Statement -- 3 Encoding -- 4 First Order Model -- 5 Experiments -- 6 Model Discussion and Generalization -- 7 Related Work -- 8 Conclusions -- A Appendix: Introduction to IDP -- References -- Learning Inference by Induction -- 1 Introduction -- 2 Learning Logical Inference -- 2.1 Learning Logics -- 2.2 Learning from 1-Step Transitions -- 2.3 Learning Deduction Rules by LF1T -- 3 Learning Non-logical Inference Rules -- 3.1 Abduction -- 3.2 Frame Axiom -- 3.3 Conversational Implicature -- 4 Discussion -- 5 Conclusion -- References -- Identification of Transition Models of Biological Systems in the Presence of Transition Noise -- 1 Introduction -- 2 Transition Identification Under Transition Noise -- 3 Empirical Evaluation -- 3.1 Problems -- 3.2 Data -- 3.3 Models -- 3.4 Algorithms and Machines -- 3.5 Method -- 3.6 Results -- 3.7 Transition Identification Worked Example: Water -- 4 Related Work -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910482960503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 13th International Conference, ILP 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings / / edited by Tamas Horváth, Akihiro Yamamoto
| Inductive Logic Programming [[electronic resource] ] : 13th International Conference, ILP 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings / / edited by Tamas Horváth, Akihiro Yamamoto |
| Edizione | [1st ed. 2003.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
| Descrizione fisica | 1 online resource (X, 406 p.) |
| Disciplina | 05.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Software engineering
Artificial intelligence Computer science Computer programming Mathematical logic Software Engineering/Programming and Operating Systems Artificial Intelligence Computer Science, general Programming Techniques Mathematical Logic and Formal Languages |
| ISBN | 3-540-39917-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- A Personal View of How Best to Apply ILP -- Agents that Reason and Learn -- Research Papers -- Mining Model Trees: A Multi-relational Approach -- Complexity Parameters for First-Order Classes -- A Multi-relational Decision Tree Learning Algorithm – Implementation and Experiments -- Applying Theory Revision to the Design of Distributed Databases -- Disjunctive Learning with a Soft-Clustering Method -- ILP for Mathematical Discovery -- An Exhaustive Matching Procedure for the Improvement of Learning Efficiency -- Efficient Data Structures for Inductive Logic Programming -- Graph Kernels and Gaussian Processes for Relational Reinforcement Learning -- On Condensation of a Clause -- A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting -- Comparative Evaluation of Approaches to Propositionalization -- Ideal Refinement of Descriptions in -Log -- Which First-Order Logic Clauses Can Be Learned Using Genetic Algorithms? -- Improved Distances for Structured Data -- Induction of Enzyme Classes from Biological Databases -- Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars -- Induction of the Effects of Actions by Monotonic Methods -- Hybrid Abductive Inductive Learning: A Generalisation of Progol -- Query Optimization in Inductive Logic Programming by Reordering Literals -- Efficient Learning of Unlabeled Term Trees with Contractible Variables from Positive Data -- Relational IBL in Music with a New Structural Similarity Measure -- An Effective Grammar-Based Compression Algorithm for Tree Structured Data. |
| Record Nr. | UNISA-996465800003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive Logic Programming : 13th International Conference, ILP 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings / / edited by Tamas Horváth, Akihiro Yamamoto
| Inductive Logic Programming : 13th International Conference, ILP 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings / / edited by Tamas Horváth, Akihiro Yamamoto |
| Edizione | [1st ed. 2003.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
| Descrizione fisica | 1 online resource (X, 406 p.) |
| Disciplina | 05.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Software engineering
Artificial intelligence Computer science Computer programming Logic, Symbolic and mathematical Software Engineering/Programming and Operating Systems Artificial Intelligence Computer Science, general Programming Techniques Mathematical Logic and Formal Languages |
| ISBN | 3-540-39917-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- A Personal View of How Best to Apply ILP -- Agents that Reason and Learn -- Research Papers -- Mining Model Trees: A Multi-relational Approach -- Complexity Parameters for First-Order Classes -- A Multi-relational Decision Tree Learning Algorithm – Implementation and Experiments -- Applying Theory Revision to the Design of Distributed Databases -- Disjunctive Learning with a Soft-Clustering Method -- ILP for Mathematical Discovery -- An Exhaustive Matching Procedure for the Improvement of Learning Efficiency -- Efficient Data Structures for Inductive Logic Programming -- Graph Kernels and Gaussian Processes for Relational Reinforcement Learning -- On Condensation of a Clause -- A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting -- Comparative Evaluation of Approaches to Propositionalization -- Ideal Refinement of Descriptions in -Log -- Which First-Order Logic Clauses Can Be Learned Using Genetic Algorithms? -- Improved Distances for Structured Data -- Induction of Enzyme Classes from Biological Databases -- Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars -- Induction of the Effects of Actions by Monotonic Methods -- Hybrid Abductive Inductive Learning: A Generalisation of Progol -- Query Optimization in Inductive Logic Programming by Reordering Literals -- Efficient Learning of Unlabeled Term Trees with Contractible Variables from Positive Data -- Relational IBL in Music with a New Structural Similarity Measure -- An Effective Grammar-Based Compression Algorithm for Tree Structured Data. |
| Record Nr. | UNINA-9910768183503321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||