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DAMP'12 : proceedings of the 7th Workshop on Declarative Aspects and Applications of Multicore Programming : January 28, 2012, Philadelphia, PA, USA
| DAMP'12 : proceedings of the 7th Workshop on Declarative Aspects and Applications of Multicore Programming : January 28, 2012, Philadelphia, PA, USA |
| Autore | Acar Umut |
| Pubbl/distr/stampa | [Place of publication not identified], : ACM, 2012 |
| Descrizione fisica | 1 online resource (56 pages) |
| Collana | ACM Conferences |
| Soggetto topico |
Engineering & Applied Sciences
Computer Science |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Declarative Aspects and Applications of Multicore Programming'12 : proceedings of the 7th Workshop on Declarative Aspects and Applications of Multicore Programming : January 28, 2012, Philadelphia, PA, USA
Proceedings of the 7th Workshop on Declarative aspects and applications of multicore programming |
| Record Nr. | UNINA-9910376152003321 |
Acar Umut
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| [Place of publication not identified], : ACM, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes
| Inductive Logic Programming [[electronic resource] ] : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XIII, 141 p. 31 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Computers Application software Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Computation by Abstract Devices Information Systems Applications (incl. Internet) |
| ISBN | 3-662-44923-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement -- 1 Introduction -- 1.1 Bayesian MIL Versus Probabilistic ILP -- 1.2 Multiple and Single Models -- 2 MetaBayes Refinement Framework -- 2.1 Setting -- 2.2 Generalised Meta-Interpreter -- 2.3 Stochastic Refinement -- 2.4 Prior, Likelihood and Posterior -- 3 Implementation -- 3.1 MetaBayes -- 3.2 MetaBayesMAP -- 3.3 MetaBayesSiLP -- 3.4 MilProbLog -- 4 Experiments -- 4.1 Binary Prediction - MetaBayes vs. MetaMap -- 4.2 Probabilistic Prediction - MetaBayes vs. MetaBayesSiLP vs. MilProbLog -- 5 Related Work -- 6 Conclusion and Further Work -- References -- On Differentially Private Inductive Logic Programming -- 1 Introduction -- 2 Preliminaries -- 2.1 Inductive Logic Programming -- 2.2 Differential Privacy -- 3 Problem Formulation -- 4 Trade-Off on Privacy and Utility -- 4.1 Our Utility Model -- 4.2 A Lower Bound on Privacy Parameter -- 5 Differentially Private ILP Algorithm -- 5.1 A Non-private ILP Algorithm -- 5.2 A Differentially Private Selection Algorithm -- 5.3 A Differentially Private Reduction Algorithm -- 5.4 Our Differentially Private ILP Algorithm -- 6 Experiment -- 7 Conclusion -- References -- Learning Through Hypothesis Refinement Using Answer Set Programming -- 1 Introduction -- 2 Background -- 2.1 Top-Directed Abductive Learning in ASP -- 3 Learning Through Hypothesis Refinement -- 3.1 Hypothesis Refinement -- 3.2 Learning a Partial Hypothesis -- 4 RASPAL: Iterative Learning by Refinement -- 4.1 Algorithms -- 5 Experiment -- 6 Conclusion and Future Work -- References -- A BDD-Based Algorithm for Learning from Interpretation Transition -- 1 Introduction -- 2 Learning from 1-Step Transitions -- 3 BDD Algorithms for LF1T -- 4 Experiments -- 5 Conclusion and Future Work -- A Appendix.
A.1 Proof of Theorem 1 -- References -- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization -- 1 Introduction -- 2 Background -- 3 Relational Imitation Learning -- 4 Relational Transfer -- 5 Experiments -- 6 Discussion and Conclusion -- References -- A Direct Policy-Search Algorithm for Relational Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Terminology -- 3.1 Blocks World -- 4 CERRLA Algorithm -- 4.1 Cross-Entropy Method -- 4.2 Rule Discovery -- 4.3 Policy-Search Process -- 5 Evaluation -- 5.1 Blocks World -- 5.2 Ms.Pac-Man -- 5.3 Carcassonne -- 6 Conclusions -- References -- AND Parallelism for ILP: The APIS System -- 1 Introduction -- 2 Background -- 2.1 Parallel Execution of Logic Programs -- 3 The APIS System -- 3.1 Redundancy Avoidance -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results and Discussion -- 5 Parallel Execution of ILP Systems -- 6 Conclusions -- References -- Generalized Counting for Lifted Variable Elimination -- 1 Introduction -- 2 Representation -- 3 Lifted Variable Elimination -- 4 Generalized Counting Formulas -- 4.1 A Motivating Example -- 4.2 Definition -- 5 Conversion Operations -- 5.1 Counting Conversion -- 5.2 Merging Counting Formulas -- 5.3 Merge-Counting -- 6 Elimination Operations -- 6.1 Sum-out by Counting -- 6.2 Aggregation -- 7 Relation to Joint Conversion -- 8 Conclusion -- References -- A FOIL-Like Method for Learning under Incompleteness and Vagueness -- 1 Introduction -- 2 Preliminaries -- 3 Learning Fuzzy EL(D) Axioms -- 3.1 The Problem Statement -- 3.2 The Solution Strategy -- 4 Related Work -- 5 Towards an Application in Tourism -- 6 Conclusions -- References -- Author Index. |
| Record Nr. | UNISA-996199679503316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive Logic Programming : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes
| Inductive Logic Programming : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XIII, 141 p. 31 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Computers Application software Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Computation by Abstract Devices Information Systems Applications (incl. Internet) |
| ISBN | 3-662-44923-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement -- 1 Introduction -- 1.1 Bayesian MIL Versus Probabilistic ILP -- 1.2 Multiple and Single Models -- 2 MetaBayes Refinement Framework -- 2.1 Setting -- 2.2 Generalised Meta-Interpreter -- 2.3 Stochastic Refinement -- 2.4 Prior, Likelihood and Posterior -- 3 Implementation -- 3.1 MetaBayes -- 3.2 MetaBayesMAP -- 3.3 MetaBayesSiLP -- 3.4 MilProbLog -- 4 Experiments -- 4.1 Binary Prediction - MetaBayes vs. MetaMap -- 4.2 Probabilistic Prediction - MetaBayes vs. MetaBayesSiLP vs. MilProbLog -- 5 Related Work -- 6 Conclusion and Further Work -- References -- On Differentially Private Inductive Logic Programming -- 1 Introduction -- 2 Preliminaries -- 2.1 Inductive Logic Programming -- 2.2 Differential Privacy -- 3 Problem Formulation -- 4 Trade-Off on Privacy and Utility -- 4.1 Our Utility Model -- 4.2 A Lower Bound on Privacy Parameter -- 5 Differentially Private ILP Algorithm -- 5.1 A Non-private ILP Algorithm -- 5.2 A Differentially Private Selection Algorithm -- 5.3 A Differentially Private Reduction Algorithm -- 5.4 Our Differentially Private ILP Algorithm -- 6 Experiment -- 7 Conclusion -- References -- Learning Through Hypothesis Refinement Using Answer Set Programming -- 1 Introduction -- 2 Background -- 2.1 Top-Directed Abductive Learning in ASP -- 3 Learning Through Hypothesis Refinement -- 3.1 Hypothesis Refinement -- 3.2 Learning a Partial Hypothesis -- 4 RASPAL: Iterative Learning by Refinement -- 4.1 Algorithms -- 5 Experiment -- 6 Conclusion and Future Work -- References -- A BDD-Based Algorithm for Learning from Interpretation Transition -- 1 Introduction -- 2 Learning from 1-Step Transitions -- 3 BDD Algorithms for LF1T -- 4 Experiments -- 5 Conclusion and Future Work -- A Appendix.
A.1 Proof of Theorem 1 -- References -- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization -- 1 Introduction -- 2 Background -- 3 Relational Imitation Learning -- 4 Relational Transfer -- 5 Experiments -- 6 Discussion and Conclusion -- References -- A Direct Policy-Search Algorithm for Relational Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Terminology -- 3.1 Blocks World -- 4 CERRLA Algorithm -- 4.1 Cross-Entropy Method -- 4.2 Rule Discovery -- 4.3 Policy-Search Process -- 5 Evaluation -- 5.1 Blocks World -- 5.2 Ms.Pac-Man -- 5.3 Carcassonne -- 6 Conclusions -- References -- AND Parallelism for ILP: The APIS System -- 1 Introduction -- 2 Background -- 2.1 Parallel Execution of Logic Programs -- 3 The APIS System -- 3.1 Redundancy Avoidance -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results and Discussion -- 5 Parallel Execution of ILP Systems -- 6 Conclusions -- References -- Generalized Counting for Lifted Variable Elimination -- 1 Introduction -- 2 Representation -- 3 Lifted Variable Elimination -- 4 Generalized Counting Formulas -- 4.1 A Motivating Example -- 4.2 Definition -- 5 Conversion Operations -- 5.1 Counting Conversion -- 5.2 Merging Counting Formulas -- 5.3 Merge-Counting -- 6 Elimination Operations -- 6.1 Sum-out by Counting -- 6.2 Aggregation -- 7 Relation to Joint Conversion -- 8 Conclusion -- References -- A FOIL-Like Method for Learning under Incompleteness and Vagueness -- 1 Introduction -- 2 Preliminaries -- 3 Learning Fuzzy EL(D) Axioms -- 3.1 The Problem Statement -- 3.2 The Solution Strategy -- 4 Related Work -- 5 Towards an Application in Tourism -- 6 Conclusions -- References -- Author Index. |
| Record Nr. | UNINA-9910484814403321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 | ||
| 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 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. | UNINA-9910484000803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 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. | UNINA-9910484000703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||