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Advances in Intelligent Data Analysis XV [[electronic resource] ] : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings / / edited by Henrik Boström, Arno Knobbe, Carlos Soares, Panagiotis Papapetrou
| Advances in Intelligent Data Analysis XV [[electronic resource] ] : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings / / edited by Henrik Boström, Arno Knobbe, Carlos Soares, Panagiotis Papapetrou |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIII, 404 p. 146 illus.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Application software Artificial intelligence Information storage and retrieval Algorithms Data mining Database Management Information Systems Applications (incl. Internet) Artificial Intelligence Information Storage and Retrieval Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery |
| ISBN | 3-319-46349-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | DSCo-NG: A Practical Language Modeling Approach for Time Series Classification -- Ranking Accuracy for Logistic-GEE models -- The Morality Machine: Tracking Moral Values in Tweets -- A Hybrid Approach for Probabilistic Relational Models Structure Learning -- On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks -- Obtaining Shape Descriptors from a Concave Hull-Based Clustering Algorithm -- Visual Perception of Discriminative Landmarks in Classified Time Series -- Spotting the Diffusion of New Psychoactive Substances over the Internet -- Feature Selection Issues in Long-Term Travel Time Prediction -- A Mean-Field Variational Bayesian Approach to Detecting Overlapping Communities with Inner Roles Using Poisson Link Generation -- Online Semi-supervised Learning for Multi-target Regression in Data streams Using AMRules -- A Toolkit for Analysis of Deep Learning Experiments -- The Optimistic Method for Model Estimation -- Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML -- Learning from the News: Predicting Entity Popularity on Twitter -- Multi-scale Kernel PCA and Its Application to Curvelet-based Feature Extraction for Mammographic Mass Characterization -- Weakly-supervised Symptom Recognition for Rare Diseases in Biomedical Text -- Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data -- Widened Learning of Bayesian Network Classifiers -- Vote Buying Detection via Independent Component Analysis -- Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining -- A Framework for Interpolating Scattered Data Using Space-filling Curves -- Privacy-Awareness of Distributed Data Clustering Algorithms Revisited -- Bi-stochastic Matrix Approximation Framework for Data Co-clustering -- Sequential Cost-Sensitive Feature Acquisition -- Explainable and Efficient Link Prediction in Real-World Network Data -- DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs -- Similarity Based Hierarchical Clustering with an Application to Text Collections -- Determining Data Relevance Using Semantic Types and Graphical Interpretation Cues -- A First Step Toward Quantifying the Climate's Information Production over the Last 68,000 Years -- HAUCA Curves for the Evaluation of Biomarker Pilot Studies with Small Sample Sizes and Large Numbers of Features -- Stability Evaluation of Event Detection Techniques for Twitter -- IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures -- An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data -- Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance -- Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering. . |
| Record Nr. | UNISA-996466250703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings / / edited by Henrik Boström, Arno Knobbe, Carlos Soares, Panagiotis Papapetrou
| Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings / / edited by Henrik Boström, Arno Knobbe, Carlos Soares, Panagiotis Papapetrou |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIII, 404 p. 146 illus.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Application software Artificial intelligence Information storage and retrieval Algorithms Data mining Database Management Information Systems Applications (incl. Internet) Artificial Intelligence Information Storage and Retrieval Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery |
| ISBN | 3-319-46349-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | DSCo-NG: A Practical Language Modeling Approach for Time Series Classification -- Ranking Accuracy for Logistic-GEE models -- The Morality Machine: Tracking Moral Values in Tweets -- A Hybrid Approach for Probabilistic Relational Models Structure Learning -- On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks -- Obtaining Shape Descriptors from a Concave Hull-Based Clustering Algorithm -- Visual Perception of Discriminative Landmarks in Classified Time Series -- Spotting the Diffusion of New Psychoactive Substances over the Internet -- Feature Selection Issues in Long-Term Travel Time Prediction -- A Mean-Field Variational Bayesian Approach to Detecting Overlapping Communities with Inner Roles Using Poisson Link Generation -- Online Semi-supervised Learning for Multi-target Regression in Data streams Using AMRules -- A Toolkit for Analysis of Deep Learning Experiments -- The Optimistic Method for Model Estimation -- Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML -- Learning from the News: Predicting Entity Popularity on Twitter -- Multi-scale Kernel PCA and Its Application to Curvelet-based Feature Extraction for Mammographic Mass Characterization -- Weakly-supervised Symptom Recognition for Rare Diseases in Biomedical Text -- Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data -- Widened Learning of Bayesian Network Classifiers -- Vote Buying Detection via Independent Component Analysis -- Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining -- A Framework for Interpolating Scattered Data Using Space-filling Curves -- Privacy-Awareness of Distributed Data Clustering Algorithms Revisited -- Bi-stochastic Matrix Approximation Framework for Data Co-clustering -- Sequential Cost-Sensitive Feature Acquisition -- Explainable and Efficient Link Prediction in Real-World Network Data -- DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs -- Similarity Based Hierarchical Clustering with an Application to Text Collections -- Determining Data Relevance Using Semantic Types and Graphical Interpretation Cues -- A First Step Toward Quantifying the Climate's Information Production over the Last 68,000 Years -- HAUCA Curves for the Evaluation of Biomarker Pilot Studies with Small Sample Sizes and Large Numbers of Features -- Stability Evaluation of Event Detection Techniques for Twitter -- IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures -- An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data -- Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance -- Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering. . |
| Record Nr. | UNINA-9910484968303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining for business applications [[electronic resource] /] / edited by Carlos Soares and Rayid Ghani
| Data mining for business applications [[electronic resource] /] / edited by Carlos Soares and Rayid Ghani |
| Pubbl/distr/stampa | Amsterdam, : IOS Press, 2010 |
| Descrizione fisica | 1 online resource (196 p.) |
| Disciplina | 658.056312 22 |
| Altri autori (Persone) |
SoaresCarlos
GhaniRayid |
| Collana | Frontiers in artificial intelligence and applications |
| Soggetto topico |
Data mining
Business - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
6612956178
1-282-95617-5 9786612956171 1-60750-633-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Data mining methodology -- pt. 2. Data mining applications of today -- pt. 3. Data mining applications of tomorrow. |
| Record Nr. | UNINA-9910460438703321 |
| Amsterdam, : IOS Press, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining for business applications [[electronic resource] /] / edited by Carlos Soares and Rayid Ghani
| Data mining for business applications [[electronic resource] /] / edited by Carlos Soares and Rayid Ghani |
| Pubbl/distr/stampa | Amsterdam, : IOS Press, 2010 |
| Descrizione fisica | 1 online resource (196 p.) |
| Disciplina | 658.056312 22 |
| Altri autori (Persone) |
SoaresCarlos
GhaniRayid |
| Collana | Frontiers in artificial intelligence and applications |
| Soggetto topico |
Data mining
Business - Data processing |
| ISBN |
6612956178
1-282-95617-5 9786612956171 1-60750-633-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Data mining methodology -- pt. 2. Data mining applications of today -- pt. 3. Data mining applications of tomorrow. |
| Record Nr. | UNINA-9910785411103321 |
| Amsterdam, : IOS Press, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares
| Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XLII, 773 p. 198 illus.) |
| Disciplina | 006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern recognition Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-23525-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996200359603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge
| Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (LVIII, 709 p. 160 illus.) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern recognition Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-23528-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Towards Declarative, Domain-OrientedData Analysis -- Sum-Product Networks: Deep Modelswith Tractable Inference -- Mining Online Networks and Communities -- Learning to Acquire Knowledge in a SmartGrid Environment -- Untangling the Web's Invisible Net -- Towards a Digital Time Machine Fueled by BigData and Social Mining -- Abstracts of Journal Track Articles -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Research Track Classification, Regression and Supervised Learning -- Data Split Strategiesfor Evolving Predictive Models -- 1 Introduction -- 2 Data Splits for Model Fitting, Selection,and Assessment -- 3 Issues with Evolving Models -- 4 Data Splits for Evolving Models -- 4.1 Parallel Dump Workflow -- 4.2 Serial Waterfall Workflow -- 4.3 Hybrid Workflow -- 5 Bias Due to Test Set Reuse -- 6 Illustration on Synthetic Data -- 7 Case Study: Paraphrase Detection -- 8 Related Work -- 9 Conclusions -- A Appendix: Bias Due to Test Set Reuse -- References -- Discriminative Interpolation for Classification of Functional Data -- 1 Introduction -- 2 Function Representations and Wavelets -- 3 Related Work -- 4 Classification by Discriminative Interpolation -- 4.1 Training Formulation -- 4.2 Testing Formulation -- 5 Experiments -- 6 Conclusion -- References -- Fast Label Embeddings via Randomized Linear Algebra -- 1 Introduction -- 1.1 Contributions -- 2 Algorithm Derivation -- 2.1 Notation -- 2.2 Background -- 2.3 Rank-Constrained Estimation and Embedding -- 2.4 Rembrandt -- 3 Related Work -- 4 Experiments -- 4.1 ALOI -- 4.2 ODP -- 4.3 LSHTC -- 5 Discussion -- References -- Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation -- 1 Introduction -- 2 General Idea -- 3 Theory -- 4 Closed form Solution for Objective and its Gradient.
5 Experiments -- 6 Conclusions -- References -- Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration -- 1 Introduction -- 2 Proper Scoring Rules -- 2.1 Scoring Rules -- 2.2 Divergence, Entropy and Properness -- 2.3 Expected Loss and Empirical Loss -- 3 Decompositions with Ideal Scores and Calibrated Scores -- 3.1 Ideal Scores Q and the Decomposition L=EL+IL -- 3.2 Calibrated Scores C and the Decomposition L=CL+RL -- 4 Adjusted Scores A and the Decomposition L=AL+PL -- 4.1 Adjustment -- 4.2 The Right Adjustment Procedure Guarantees Decreased Loss -- 5 Decomposition Theorems and Terminology -- 5.1 Decompositions with S,C,Q,Y -- 5.2 Decompositions with S,A,C,Q,Y and Terminology -- 6 Algorithms and Experiments -- 7 Related Work -- 8 Conclusions -- References -- Parameter Learning of Bayesian Network Classifiers Under Computational Constraints -- 1 Introduction -- 2 Related Work -- 3 Background and Notation -- 4 Algorithms for Online Learning of Reduced-Precision Parameters -- 4.1 Learning Maximum Likelihood Parameters -- 4.2 Learning Maximum Margin Parameters -- 5 Experiments -- 5.1 Datasets -- 5.2 Results -- 6 Discussions -- References -- Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning -- 1 Introduction -- 2 Multi-label Classification -- 3 Model Description -- 3.1 Joint Space Embeddings -- 3.2 Learning with Hierarchical Structures Over Labels -- 3.3 Efficient Gradients Computation -- 3.4 Label Ranking to Binary Predictions -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Learning All Labels Together -- 5.2 Learning to Predict Unseen Labels -- 6 Pretrained Label Embeddings as Good Initial Guess -- 6.1 Understanding Label Embeddings -- 6.2 Results -- 7 Conclusions -- Regression with Linear Factored Functions -- 1 Introduction -- 1.1 Kernel Regression. 1.2 Factored Basis Functions -- 2 Regression -- 3 Linear Factored Functions -- 3.1 Function Class -- 3.2 Constraints -- 3.3 Regularization -- 3.4 Optimization -- 4 Empirical Evaluation -- 4.1 Demonstration -- 4.2 Evaluation -- 5 Discussion -- Appendix A LFF Definition and Properties -- Appendix B Inner Loop Derivation -- Appendix C Proofs of the Propositions -- References -- Ridge Regression, Hubness, and Zero-Shot Learning -- 1 Introduction -- 1.1 Background -- 1.2 Research Objective and Contributions -- 2 Zero-Shot Learning as a Regression Problem -- 3 Hubness Phenomenon and the Variance of Data -- 4 Hubness in Regression-Based Zero-Shot Learning -- 4.1 Shrinkage of Projected Objects -- 4.2 Influence of Shrinkage on Nearest Neighbor Search -- 4.3 Additional Argument for Placing Target Objects Closer to the Origin -- 4.4 Summary of the Proposed Approach -- 5 Related Work -- 6 Experiments -- 6.1 Experimental Setups -- 6.2 Task Descriptions and Datasets -- 6.3 Experimental Results -- 7 Conclusion -- References -- Solving Prediction Games with Parallel Batch Gradient Descent -- 1 Introduction -- 2 Problem Setting and Data Transformation Model -- 3 Analysis of Equilibrium Points -- 3.1 Existence of Equilibrium Points -- 3.2 Uniqueness of Equilibrium Points -- 4 Finding the Unique Equilibrium Point Efficiently -- 4.1 Inexact Line Search -- 4.2 Arrow-Hurwicz-Uzawa Method -- 4.3 Parallelized Methods -- 5 Experimental Results -- 5.1 Reference Methods -- 5.2 Performance of the Parameterized Transformation Model -- 5.3 Optimization Algorithms -- 5.4 Parallelized Models -- 6 Conclusion -- References -- Structured Regularizer for Neural Higher-Order Sequence Models -- 1 Introduction -- 2 Related Work -- 3 Higher-Order Conditional Random Fields -- 3.1 Parameter Learning -- 3.2 Forward Algorithm for 2nd-Order CRFs -- 4 Structured Regularizer -- 5 Experiments. 5.1 TIMIT Data Set -- 5.2 Experimental Setup -- 5.3 Labeling Results Using Only MLP Networks -- 5.4 Labeling Results Using LC-CRFs with Linear or Neural Higher-Order Factors -- 6 Conclusion -- References -- Versatile Decision Trees for Learning Over Multiple Contexts -- 1 Introduction -- 2 Dataset Shift -- 3 Versatile Decision Trees -- 3.1 Constructing Splits Using Percentiles -- 3.2 Adapting for Output Shifts -- 3.3 Versatile Model for Decision Trees -- 4 Experimental Results -- 4.1 Generating Synthetic Shifts -- 4.2 Results of the Synthetic Shifts -- 4.3 Results on Non-synthetic Shifts -- 5 Conclusion -- References -- When is Undersampling Effective in Unbalanced Classification Tasks? -- 1 Introduction -- 2 The Warping Effect of Undersampling on the Posterior Probability -- 3 The Interaction Between Warping and Variance of the Estimator -- 4 Experimental Validation -- 4.1 Synthetic Datasets -- 4.2 Real Datasets -- 5 Conclusion -- References -- Clustering and Unsupervised Learning -- A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons -- 1 Introduction -- 2 Related Work -- 3 Kernel Learning with Relative Distances -- 3.1 Basic Definitions -- 3.2 Relative Distance Constraints -- 3.3 Extension to a Kernel Space -- 3.4 Log Determinant Divergence for Kernel Learning -- 3.5 Problem Definition -- 4 Semi-supervised Kernel Learning -- 4.1 Bregman Projections for Constrained Optimization -- 4.2 Semi-supervised Kernel Learning with Relative Comparisons -- Selecting the Bandwidth Parameter. -- Semi-Supervised Kernel Learning with Relative Comparisons. -- Clustering Method. -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Relative Constraints vs. Pairwise Constraints -- 5.3 Multi-resolution Analysis -- 5.4 Generalization Performance -- 5.5 Effect of Equality Constraints -- 6 Conclusion -- References. Bayesian Active Clustering with Pairwise Constraints -- 1 Introduction -- 2 Problem Statement -- 3 Bayesian Active Clustering -- 3.1 The Bayesian Clustering Model -- Marginalization of Cluster Labels. -- 3.2 Active Query Selection -- Selection Criteria. -- Computing the Selection Objectives. -- 3.3 The Sequential MCMC Sampling of W -- 3.4 Find the MAP Solution -- 4 Experiments -- 4.1 Dataset and Setup -- 4.2 Effectiveness of the Proposed Clustering Model -- 4.3 Effectiveness of the Overall Active Clustering Model -- 4.4 Analysis of the Acyclic Graph Restriction -- 5 Related Work -- 6 Conclusion -- References -- ConDist: A Context-Driven Categorical Distance Measure -- 1 Introduction -- 2 Related Work -- 3 The Distance Measure ConDist -- 3.1 Definition of ConDist -- 3.2 Attribute Distance dX -- 3.3 Attribute Weighting Function wX -- 3.4 Correlation, Context and Impact -- 3.5 Heterogeneous Data Sets -- 4 Experiments -- 4.1 Evaluation Methodology -- 4.2 Experiment 1 -- Context Attribute Selection -- 4.3 Experiment 2 -- Comparison in the Context of Classification -- 4.4 Experiment 3 -- Comparison in the Context of Clustering -- 5 Discussion -- 5.1 Experiment 1 -- Context Attribute Selection -- 5.2 Experiment 2 -- Comparison in the Context of Classification -- 5.3 Experiment 3 -- Comparison in the Context of Clustering -- 6 Summary -- References -- Discovering Opinion Spammer Groups by Network Footprints -- 1 Introduction -- 2 Measuring Network Footprints -- 2.1 Neighbor Diversity of Nodes -- 2.2 Self-Similarity in Real-World Graphs -- 2.3 NFS Measure -- 3 Detecting Spammer Groups -- 4 Evaluation -- 4.1 Performance of NFS on Synthetic Data -- 4.2 Performance of GroupStrainer on Synthetic Data -- 4.3 Results on Real-World Data -- 5 Related Work -- 6 Conclusion -- References -- Gamma Process Poisson Factorization for Joint Modeling of Network and Documents. 1 Introduction. |
| Record Nr. | UNISA-996200359403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge
| Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (LVIII, 709 p. 160 illus.) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern perception Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-23528-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Towards Declarative, Domain-OrientedData Analysis -- Sum-Product Networks: Deep Modelswith Tractable Inference -- Mining Online Networks and Communities -- Learning to Acquire Knowledge in a SmartGrid Environment -- Untangling the Web's Invisible Net -- Towards a Digital Time Machine Fueled by BigData and Social Mining -- Abstracts of Journal Track Articles -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Research Track Classification, Regression and Supervised Learning -- Data Split Strategiesfor Evolving Predictive Models -- 1 Introduction -- 2 Data Splits for Model Fitting, Selection,and Assessment -- 3 Issues with Evolving Models -- 4 Data Splits for Evolving Models -- 4.1 Parallel Dump Workflow -- 4.2 Serial Waterfall Workflow -- 4.3 Hybrid Workflow -- 5 Bias Due to Test Set Reuse -- 6 Illustration on Synthetic Data -- 7 Case Study: Paraphrase Detection -- 8 Related Work -- 9 Conclusions -- A Appendix: Bias Due to Test Set Reuse -- References -- Discriminative Interpolation for Classification of Functional Data -- 1 Introduction -- 2 Function Representations and Wavelets -- 3 Related Work -- 4 Classification by Discriminative Interpolation -- 4.1 Training Formulation -- 4.2 Testing Formulation -- 5 Experiments -- 6 Conclusion -- References -- Fast Label Embeddings via Randomized Linear Algebra -- 1 Introduction -- 1.1 Contributions -- 2 Algorithm Derivation -- 2.1 Notation -- 2.2 Background -- 2.3 Rank-Constrained Estimation and Embedding -- 2.4 Rembrandt -- 3 Related Work -- 4 Experiments -- 4.1 ALOI -- 4.2 ODP -- 4.3 LSHTC -- 5 Discussion -- References -- Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation -- 1 Introduction -- 2 General Idea -- 3 Theory -- 4 Closed form Solution for Objective and its Gradient.
5 Experiments -- 6 Conclusions -- References -- Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration -- 1 Introduction -- 2 Proper Scoring Rules -- 2.1 Scoring Rules -- 2.2 Divergence, Entropy and Properness -- 2.3 Expected Loss and Empirical Loss -- 3 Decompositions with Ideal Scores and Calibrated Scores -- 3.1 Ideal Scores Q and the Decomposition L=EL+IL -- 3.2 Calibrated Scores C and the Decomposition L=CL+RL -- 4 Adjusted Scores A and the Decomposition L=AL+PL -- 4.1 Adjustment -- 4.2 The Right Adjustment Procedure Guarantees Decreased Loss -- 5 Decomposition Theorems and Terminology -- 5.1 Decompositions with S,C,Q,Y -- 5.2 Decompositions with S,A,C,Q,Y and Terminology -- 6 Algorithms and Experiments -- 7 Related Work -- 8 Conclusions -- References -- Parameter Learning of Bayesian Network Classifiers Under Computational Constraints -- 1 Introduction -- 2 Related Work -- 3 Background and Notation -- 4 Algorithms for Online Learning of Reduced-Precision Parameters -- 4.1 Learning Maximum Likelihood Parameters -- 4.2 Learning Maximum Margin Parameters -- 5 Experiments -- 5.1 Datasets -- 5.2 Results -- 6 Discussions -- References -- Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning -- 1 Introduction -- 2 Multi-label Classification -- 3 Model Description -- 3.1 Joint Space Embeddings -- 3.2 Learning with Hierarchical Structures Over Labels -- 3.3 Efficient Gradients Computation -- 3.4 Label Ranking to Binary Predictions -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Learning All Labels Together -- 5.2 Learning to Predict Unseen Labels -- 6 Pretrained Label Embeddings as Good Initial Guess -- 6.1 Understanding Label Embeddings -- 6.2 Results -- 7 Conclusions -- Regression with Linear Factored Functions -- 1 Introduction -- 1.1 Kernel Regression. 1.2 Factored Basis Functions -- 2 Regression -- 3 Linear Factored Functions -- 3.1 Function Class -- 3.2 Constraints -- 3.3 Regularization -- 3.4 Optimization -- 4 Empirical Evaluation -- 4.1 Demonstration -- 4.2 Evaluation -- 5 Discussion -- Appendix A LFF Definition and Properties -- Appendix B Inner Loop Derivation -- Appendix C Proofs of the Propositions -- References -- Ridge Regression, Hubness, and Zero-Shot Learning -- 1 Introduction -- 1.1 Background -- 1.2 Research Objective and Contributions -- 2 Zero-Shot Learning as a Regression Problem -- 3 Hubness Phenomenon and the Variance of Data -- 4 Hubness in Regression-Based Zero-Shot Learning -- 4.1 Shrinkage of Projected Objects -- 4.2 Influence of Shrinkage on Nearest Neighbor Search -- 4.3 Additional Argument for Placing Target Objects Closer to the Origin -- 4.4 Summary of the Proposed Approach -- 5 Related Work -- 6 Experiments -- 6.1 Experimental Setups -- 6.2 Task Descriptions and Datasets -- 6.3 Experimental Results -- 7 Conclusion -- References -- Solving Prediction Games with Parallel Batch Gradient Descent -- 1 Introduction -- 2 Problem Setting and Data Transformation Model -- 3 Analysis of Equilibrium Points -- 3.1 Existence of Equilibrium Points -- 3.2 Uniqueness of Equilibrium Points -- 4 Finding the Unique Equilibrium Point Efficiently -- 4.1 Inexact Line Search -- 4.2 Arrow-Hurwicz-Uzawa Method -- 4.3 Parallelized Methods -- 5 Experimental Results -- 5.1 Reference Methods -- 5.2 Performance of the Parameterized Transformation Model -- 5.3 Optimization Algorithms -- 5.4 Parallelized Models -- 6 Conclusion -- References -- Structured Regularizer for Neural Higher-Order Sequence Models -- 1 Introduction -- 2 Related Work -- 3 Higher-Order Conditional Random Fields -- 3.1 Parameter Learning -- 3.2 Forward Algorithm for 2nd-Order CRFs -- 4 Structured Regularizer -- 5 Experiments. 5.1 TIMIT Data Set -- 5.2 Experimental Setup -- 5.3 Labeling Results Using Only MLP Networks -- 5.4 Labeling Results Using LC-CRFs with Linear or Neural Higher-Order Factors -- 6 Conclusion -- References -- Versatile Decision Trees for Learning Over Multiple Contexts -- 1 Introduction -- 2 Dataset Shift -- 3 Versatile Decision Trees -- 3.1 Constructing Splits Using Percentiles -- 3.2 Adapting for Output Shifts -- 3.3 Versatile Model for Decision Trees -- 4 Experimental Results -- 4.1 Generating Synthetic Shifts -- 4.2 Results of the Synthetic Shifts -- 4.3 Results on Non-synthetic Shifts -- 5 Conclusion -- References -- When is Undersampling Effective in Unbalanced Classification Tasks? -- 1 Introduction -- 2 The Warping Effect of Undersampling on the Posterior Probability -- 3 The Interaction Between Warping and Variance of the Estimator -- 4 Experimental Validation -- 4.1 Synthetic Datasets -- 4.2 Real Datasets -- 5 Conclusion -- References -- Clustering and Unsupervised Learning -- A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons -- 1 Introduction -- 2 Related Work -- 3 Kernel Learning with Relative Distances -- 3.1 Basic Definitions -- 3.2 Relative Distance Constraints -- 3.3 Extension to a Kernel Space -- 3.4 Log Determinant Divergence for Kernel Learning -- 3.5 Problem Definition -- 4 Semi-supervised Kernel Learning -- 4.1 Bregman Projections for Constrained Optimization -- 4.2 Semi-supervised Kernel Learning with Relative Comparisons -- Selecting the Bandwidth Parameter. -- Semi-Supervised Kernel Learning with Relative Comparisons. -- Clustering Method. -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Relative Constraints vs. Pairwise Constraints -- 5.3 Multi-resolution Analysis -- 5.4 Generalization Performance -- 5.5 Effect of Equality Constraints -- 6 Conclusion -- References. Bayesian Active Clustering with Pairwise Constraints -- 1 Introduction -- 2 Problem Statement -- 3 Bayesian Active Clustering -- 3.1 The Bayesian Clustering Model -- Marginalization of Cluster Labels. -- 3.2 Active Query Selection -- Selection Criteria. -- Computing the Selection Objectives. -- 3.3 The Sequential MCMC Sampling of W -- 3.4 Find the MAP Solution -- 4 Experiments -- 4.1 Dataset and Setup -- 4.2 Effectiveness of the Proposed Clustering Model -- 4.3 Effectiveness of the Overall Active Clustering Model -- 4.4 Analysis of the Acyclic Graph Restriction -- 5 Related Work -- 6 Conclusion -- References -- ConDist: A Context-Driven Categorical Distance Measure -- 1 Introduction -- 2 Related Work -- 3 The Distance Measure ConDist -- 3.1 Definition of ConDist -- 3.2 Attribute Distance dX -- 3.3 Attribute Weighting Function wX -- 3.4 Correlation, Context and Impact -- 3.5 Heterogeneous Data Sets -- 4 Experiments -- 4.1 Evaluation Methodology -- 4.2 Experiment 1 -- Context Attribute Selection -- 4.3 Experiment 2 -- Comparison in the Context of Classification -- 4.4 Experiment 3 -- Comparison in the Context of Clustering -- 5 Discussion -- 5.1 Experiment 1 -- Context Attribute Selection -- 5.2 Experiment 2 -- Comparison in the Context of Classification -- 5.3 Experiment 3 -- Comparison in the Context of Clustering -- 6 Summary -- References -- Discovering Opinion Spammer Groups by Network Footprints -- 1 Introduction -- 2 Measuring Network Footprints -- 2.1 Neighbor Diversity of Nodes -- 2.2 Self-Similarity in Real-World Graphs -- 2.3 NFS Measure -- 3 Detecting Spammer Groups -- 4 Evaluation -- 4.1 Performance of NFS on Synthetic Data -- 4.2 Performance of GroupStrainer on Synthetic Data -- 4.3 Results on Real-World Data -- 5 Related Work -- 6 Conclusion -- References -- Gamma Process Poisson Factorization for Joint Modeling of Network and Documents. 1 Introduction. |
| Record Nr. | UNINA-9910484000803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares
| Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XLII, 773 p. 198 illus.) |
| Disciplina | 006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern perception Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-23525-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484000703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IX
| Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IX |
| Autore | Dutra Inês |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (887 pages) |
| Altri autori (Persone) |
PechenizkiyMykola
CortezPaulo PashamiSepideh JorgeAlípio M SoaresCarlos AbreuPedro H GamaJoão |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-032-06118-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996678674203316 |
Dutra Inês
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| Cham : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part II
| Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part II |
| Autore | Ribeiro Rita P |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (939 pages) |
| Altri autori (Persone) |
PfahringerBernhard
JapkowiczNathalie LarrañagaPedro JorgeAlípio M SoaresCarlos AbreuPedro H GamaJoão |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-032-05981-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996678673803316 |
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Ribeiro Rita P
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| Cham : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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