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Autorenporträt
Jean-Francois Boulicaut, INSA Lyon, France / Floriana Esposito, University of Bari, Italy / Fosca Giannotti, ISTI - CNR, Pisa, Italy / Dino Pedreschi, University of Pisa, Italy
Inhaltsangabe
Invited Papers.- Random Matrices in Data Analysis.- Data Privacy.- Breaking Through the Syntax Barrier: Searching with Entities and Relations.- Real-World Learning with Markov Logic Networks.- Strength in Diversity: The Advance of Data Analysis.- Contributed Papers.- Filtered Reinforcement Learning.- Applying Support Vector Machines to Imbalanced Datasets.- Sensitivity Analysis of the Result in Binary Decision Trees.- A Boosting Approach to Multiple Instance Learning.- An Experimental Study of Different Approaches to Reinforcement Learning in Common Interest Stochastic Games.- Learning from Message Pairs for Automatic Email Answering.- Concept Formation in Expressive Description Logics.- Multi-level Boundary Classification for Information Extraction.- An Analysis of Stopping and Filtering Criteria for Rule Learning.- Adaptive Online Time Allocation to Search Algorithms.- Model Approximation for HEXQ Hierarchical Reinforcement Learning.- Iterative Ensemble Classification for RelationalData: A Case Study of Semantic Web Services.- Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics.- Experiments in Value Function Approximation with Sparse Support Vector Regression.- Constructive Induction for Classifying Time Series.- Fisher Kernels for Logical Sequences.- The Enron Corpus: A New Dataset for Email Classification Research.- Margin Maximizing Discriminant Analysis.- Multi-objective Classification with Info-Fuzzy Networks.- Improving Progressive Sampling via Meta-learning on Learning Curves.- Methods for Rule Conflict Resolution.- An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes Risk.- Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection.- Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework.- Justification-Based Selection of Training Examples for Case Base Reduction.- Using Feature Conjunctions Across Examples for Learning Pairwise Classifiers.- Feature Selection Filters Based on the Permutation Test.- Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning.- Improving Random Forests.- The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering.- Using String Kernels to Identify Famous Performers from Their Playing Style.- Associative Clustering.- Learning to Fly Simple and Robust.- Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data.- Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees.- Inducing Polynomial Equations for Regression.- Efficient Hyperkernel Learning Using Second-Order Cone Programming.- Effective Voting of Heterogeneous Classifiers.- Convergence and Divergence in Standard and Averaging Reinforcement Learning.- Document Representation for One-Class SVM.- Naive Bayesian Classifiers for Ranking.- Conditional Independence Trees.- Exploiting Unlabeled Data in Content-BasedImage Retrieval.- Population Diversity in Permutation-Based Genetic Algorithm.- Simultaneous Concept Learning of Fuzzy Rules.- Posters.- SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous Data.- Estimating Attributed Central Orders.- Batch Reinforcement Learning with State Importance.- Explicit Local Models: Towards "Optimal" Optimization Algorithms.- An Intelligent Model for the Signorini Contact Problem in Belt Grinding Processes.- Cluster-Grouping: From Subgroup Discovery to Clustering.
Invited Papers.- Random Matrices in Data Analysis.- Data Privacy.- Breaking Through the Syntax Barrier: Searching with Entities and Relations.- Real-World Learning with Markov Logic Networks.- Strength in Diversity: The Advance of Data Analysis.- Contributed Papers.- Filtered Reinforcement Learning.- Applying Support Vector Machines to Imbalanced Datasets.- Sensitivity Analysis of the Result in Binary Decision Trees.- A Boosting Approach to Multiple Instance Learning.- An Experimental Study of Different Approaches to Reinforcement Learning in Common Interest Stochastic Games.- Learning from Message Pairs for Automatic Email Answering.- Concept Formation in Expressive Description Logics.- Multi-level Boundary Classification for Information Extraction.- An Analysis of Stopping and Filtering Criteria for Rule Learning.- Adaptive Online Time Allocation to Search Algorithms.- Model Approximation for HEXQ Hierarchical Reinforcement Learning.- Iterative Ensemble Classification for RelationalData: A Case Study of Semantic Web Services.- Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics.- Experiments in Value Function Approximation with Sparse Support Vector Regression.- Constructive Induction for Classifying Time Series.- Fisher Kernels for Logical Sequences.- The Enron Corpus: A New Dataset for Email Classification Research.- Margin Maximizing Discriminant Analysis.- Multi-objective Classification with Info-Fuzzy Networks.- Improving Progressive Sampling via Meta-learning on Learning Curves.- Methods for Rule Conflict Resolution.- An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes Risk.- Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection.- Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework.- Justification-Based Selection of Training Examples for Case Base Reduction.- Using Feature Conjunctions Across Examples for Learning Pairwise Classifiers.- Feature Selection Filters Based on the Permutation Test.- Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning.- Improving Random Forests.- The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering.- Using String Kernels to Identify Famous Performers from Their Playing Style.- Associative Clustering.- Learning to Fly Simple and Robust.- Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data.- Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees.- Inducing Polynomial Equations for Regression.- Efficient Hyperkernel Learning Using Second-Order Cone Programming.- Effective Voting of Heterogeneous Classifiers.- Convergence and Divergence in Standard and Averaging Reinforcement Learning.- Document Representation for One-Class SVM.- Naive Bayesian Classifiers for Ranking.- Conditional Independence Trees.- Exploiting Unlabeled Data in Content-BasedImage Retrieval.- Population Diversity in Permutation-Based Genetic Algorithm.- Simultaneous Concept Learning of Fuzzy Rules.- Posters.- SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous Data.- Estimating Attributed Central Orders.- Batch Reinforcement Learning with State Importance.- Explicit Local Models: Towards "Optimal" Optimization Algorithms.- An Intelligent Model for the Signorini Contact Problem in Belt Grinding Processes.- Cluster-Grouping: From Subgroup Discovery to Clustering.
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