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Machine Learning: ECML 2002 [[electronic resource] ] : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen
| Machine Learning: ECML 2002 [[electronic resource] ] : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 |
| Descrizione fisica | 1 online resource (XIV, 538 p.) |
| Disciplina | 006.3/1 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Mathematical logic Artificial Intelligence Algorithm Analysis and Problem Complexity Mathematical Logic and Formal Languages |
| ISBN | 3-540-36755-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Contributed Papers -- Convergent Gradient Ascent in General-Sum Games -- Revising Engineering Models: Combining Computational Discovery with Knowledge -- Variational Extensions to EM and Multinomial PCA -- Learning and Inference for Clause Identification -- An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks -- Variance Optimized Bagging -- How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code -- Sparse Online Greedy Support Vector Regression -- Pairwise Classification as an Ensemble Technique -- RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood -- Using Hard Classifiers to Estimate Conditional Class Probabilities -- Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner -- Scaling Boosting by Margin-Based Inclusion of Features and Relations -- Multiclass Alternating Decision Trees -- Possibilistic Induction in Decision-Tree Learning -- Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains -- Collaborative Learning of Term-Based Concepts for Automatic Query Expansion -- Learning to Play a Highly Complex Game from Human Expert Games -- Reliable Classifications with Machine Learning -- Robustness Analyses of Instance-Based Collaborative Recommendation -- iBoost: Boosting Using an instance-Based Exponential Weighting Scheme -- Towards a Simple Clustering Criterion Based on Minimum Length Encoding -- Class Probability Estimation and Cost-Sensitive Classification Decisions -- On-Line Support Vector Machine Regression -- Q-Cut—Dynamic Discovery of Sub-goals in Reinforcement Learning -- A Multistrategy Approach to the Classification of Phases in Business Cycles -- A Robust Boosting Algorithm -- Case Exchange Strategies in Multiagent Learning -- Inductive Confidence Machines for Regression -- Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique -- Propagation of Q-values in Tabular TD(?) -- Transductive Confidence Machines for Pattern Recognition -- Characterizing Markov Decision Processes -- Phase Transitions and Stochastic Local Search in k-Term DNF Learning -- Discriminative Clustering: Optimal Contingency Tables by Learning Metrics -- Boosting Density Function Estimators -- Ranking with Predictive Clustering Trees -- Support Vector Machines for Polycategorical Classification -- Learning Classification with Both Labeled and Unlabeled Data -- An Information Geometric Perspective on Active Learning -- Stacking with an Extended Set of Meta-level Attributes and MLR -- Invited Papers -- Finding Hidden Factors Using Independent Component Analysis -- Reasoning with Classifiers -- A Kernel Approach for Learning from almost Orthogonal Patterns -- Learning with Mixture Models: Concepts and Applications. |
| Record Nr. | UNISA-996466348003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning: ECML 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen
| Machine Learning: ECML 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 |
| Descrizione fisica | 1 online resource (XIV, 538 p.) |
| Disciplina | 006.3/1 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Mathematical logic Artificial Intelligence Algorithm Analysis and Problem Complexity Mathematical Logic and Formal Languages |
| ISBN | 3-540-36755-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Contributed Papers -- Convergent Gradient Ascent in General-Sum Games -- Revising Engineering Models: Combining Computational Discovery with Knowledge -- Variational Extensions to EM and Multinomial PCA -- Learning and Inference for Clause Identification -- An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks -- Variance Optimized Bagging -- How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code -- Sparse Online Greedy Support Vector Regression -- Pairwise Classification as an Ensemble Technique -- RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood -- Using Hard Classifiers to Estimate Conditional Class Probabilities -- Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner -- Scaling Boosting by Margin-Based Inclusion of Features and Relations -- Multiclass Alternating Decision Trees -- Possibilistic Induction in Decision-Tree Learning -- Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains -- Collaborative Learning of Term-Based Concepts for Automatic Query Expansion -- Learning to Play a Highly Complex Game from Human Expert Games -- Reliable Classifications with Machine Learning -- Robustness Analyses of Instance-Based Collaborative Recommendation -- iBoost: Boosting Using an instance-Based Exponential Weighting Scheme -- Towards a Simple Clustering Criterion Based on Minimum Length Encoding -- Class Probability Estimation and Cost-Sensitive Classification Decisions -- On-Line Support Vector Machine Regression -- Q-Cut—Dynamic Discovery of Sub-goals in Reinforcement Learning -- A Multistrategy Approach to the Classification of Phases in Business Cycles -- A Robust Boosting Algorithm -- Case Exchange Strategies in Multiagent Learning -- Inductive Confidence Machines for Regression -- Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique -- Propagation of Q-values in Tabular TD(?) -- Transductive Confidence Machines for Pattern Recognition -- Characterizing Markov Decision Processes -- Phase Transitions and Stochastic Local Search in k-Term DNF Learning -- Discriminative Clustering: Optimal Contingency Tables by Learning Metrics -- Boosting Density Function Estimators -- Ranking with Predictive Clustering Trees -- Support Vector Machines for Polycategorical Classification -- Learning Classification with Both Labeled and Unlabeled Data -- An Information Geometric Perspective on Active Learning -- Stacking with an Extended Set of Meta-level Attributes and MLR -- Invited Papers -- Finding Hidden Factors Using Independent Component Analysis -- Reasoning with Classifiers -- A Kernel Approach for Learning from almost Orthogonal Patterns -- Learning with Mixture Models: Concepts and Applications. |
| Record Nr. | UNINA-9910143874703321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Principles of Data Mining and Knowledge Discovery [[electronic resource] ] : 6th European Conference, PKDD 2002, Helsinki, Finland, August 19–23, 2002, Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen
| Principles of Data Mining and Knowledge Discovery [[electronic resource] ] : 6th European Conference, PKDD 2002, Helsinki, Finland, August 19–23, 2002, Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 |
| Descrizione fisica | 1 online resource (XIV, 514 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Database management
Artificial intelligence Mathematical logic Mathematical statistics Natural language processing (Computer science) Information storage and retrieval Database Management Artificial Intelligence Mathematical Logic and Formal Languages Probability and Statistics in Computer Science Natural Language Processing (NLP) Information Storage and Retrieval |
| ISBN | 3-540-45681-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Contributed Papers -- Optimized Substructure Discovery for Semi-structured Data -- Fast Outlier Detection in High Dimensional Spaces -- Data Mining in Schizophrenia Research — Preliminary Analysis -- Fast Algorithms for Mining Emerging Patterns -- On the Discovery of Weak Periodicities in Large Time Series -- The Need for Low Bias Algorithms in Classification Learning from Large Data Sets -- Mining All Non-derivable Frequent Itemsets -- Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance -- Finding Association Rules with Some Very Frequent Attributes -- Unsupervised Learning: Self-aggregation in Scaled Principal Component Space* -- A Classification Approach for Prediction of Target Events in Temporal Sequences -- Privacy-Oriented Data Mining by Proof Checking -- Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification -- Generating Actionable Knowledge by Expert-Guided Subgroup Discovery -- Clustering Transactional Data -- Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases -- Association Rules for Expressing Gradual Dependencies -- Support Approximations Using Bonferroni-Type Inequalities -- Using Condensed Representations for Interactive Association Rule Mining -- Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting -- Dependency Detection in MobiMine and Random Matrices -- Long-Term Learning for Web Search Engines -- Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database -- Involving Aggregate Functions in Multi-relational Search -- Information Extraction in Structured Documents Using Tree Automata Induction -- Algebraic Techniques for Analysis of Large Discrete-Valued Datasets -- Geography of Di.erences between Two Classes of Data -- Rule Induction for Classification of Gene Expression Array Data -- Clustering Ontology-Based Metadata in the Semantic Web -- Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases -- SVM Classification Using Sequences of Phonemes and Syllables -- A Novel Web Text Mining Method Using the Discrete Cosine Transform -- A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases -- Answering the Most Correlated N Association Rules Efficiently -- Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model -- Efficiently Mining Approximate Models of Associations in Evolving Databases -- Explaining Predictions from a Neural Network Ensemble One at a Time -- Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD -- Separability Index in Supervised Learning -- Invited Papers -- Finding Hidden Factors Using Independent Component Analysis -- Reasoning with Classifiers* -- A Kernel Approach for Learning from Almost Orthogonal Patterns -- Learning with Mixture Models: Concepts and Applications. |
| Record Nr. | UNISA-996466219803316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Principles of Data Mining and Knowledge Discovery : 6th European Conference, PKDD 2002, Helsinki, Finland, August 19–23, 2002, Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen
| Principles of Data Mining and Knowledge Discovery : 6th European Conference, PKDD 2002, Helsinki, Finland, August 19–23, 2002, Proceedings / / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 |
| Descrizione fisica | 1 online resource (XIV, 514 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Database management
Artificial intelligence Mathematical logic Mathematical statistics Natural language processing (Computer science) Information storage and retrieval Database Management Artificial Intelligence Mathematical Logic and Formal Languages Probability and Statistics in Computer Science Natural Language Processing (NLP) Information Storage and Retrieval |
| ISBN | 3-540-45681-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Contributed Papers -- Optimized Substructure Discovery for Semi-structured Data -- Fast Outlier Detection in High Dimensional Spaces -- Data Mining in Schizophrenia Research — Preliminary Analysis -- Fast Algorithms for Mining Emerging Patterns -- On the Discovery of Weak Periodicities in Large Time Series -- The Need for Low Bias Algorithms in Classification Learning from Large Data Sets -- Mining All Non-derivable Frequent Itemsets -- Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance -- Finding Association Rules with Some Very Frequent Attributes -- Unsupervised Learning: Self-aggregation in Scaled Principal Component Space* -- A Classification Approach for Prediction of Target Events in Temporal Sequences -- Privacy-Oriented Data Mining by Proof Checking -- Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification -- Generating Actionable Knowledge by Expert-Guided Subgroup Discovery -- Clustering Transactional Data -- Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases -- Association Rules for Expressing Gradual Dependencies -- Support Approximations Using Bonferroni-Type Inequalities -- Using Condensed Representations for Interactive Association Rule Mining -- Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting -- Dependency Detection in MobiMine and Random Matrices -- Long-Term Learning for Web Search Engines -- Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database -- Involving Aggregate Functions in Multi-relational Search -- Information Extraction in Structured Documents Using Tree Automata Induction -- Algebraic Techniques for Analysis of Large Discrete-Valued Datasets -- Geography of Di.erences between Two Classes of Data -- Rule Induction for Classification of Gene Expression Array Data -- Clustering Ontology-Based Metadata in the Semantic Web -- Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases -- SVM Classification Using Sequences of Phonemes and Syllables -- A Novel Web Text Mining Method Using the Discrete Cosine Transform -- A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases -- Answering the Most Correlated N Association Rules Efficiently -- Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model -- Efficiently Mining Approximate Models of Associations in Evolving Databases -- Explaining Predictions from a Neural Network Ensemble One at a Time -- Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD -- Separability Index in Supervised Learning -- Invited Papers -- Finding Hidden Factors Using Independent Component Analysis -- Reasoning with Classifiers* -- A Kernel Approach for Learning from Almost Orthogonal Patterns -- Learning with Mixture Models: Concepts and Applications. |
| Record Nr. | UNINA-9910143906103321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||