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Machine learning : ECML 2007 : 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 : proceedings / / edited by Joost N. Kok [and four others]
| Machine learning : ECML 2007 : 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 : proceedings / / edited by Joost N. Kok [and four others] |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (XXIV, 812 p.) |
| Disciplina | 006.31 |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence ;4701 |
| Soggetto topico | Machine learning |
| ISBN | 3-540-74958-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Learning, Information Extraction and the Web -- Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation -- Mining Queries -- Adventures in Personalized Information Access -- Long Papers -- Statistical Debugging Using Latent Topic Models -- Learning Balls of Strings with Correction Queries -- Neighborhood-Based Local Sensitivity -- Approximating Gaussian Processes with -Matrices -- Learning Metrics Between Tree Structured Data: Application to Image Recognition -- Shrinkage Estimator for Bayesian Network Parameters -- Level Learning Set: A Novel Classifier Based on Active Contour Models -- Learning Partially Observable Markov Models from First Passage Times -- Context Sensitive Paraphrasing with a Global Unsupervised Classifier -- Dual Strategy Active Learning -- Decision Tree Instability and Active Learning -- Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering -- The Cost of Learning Directed Cuts -- Spectral Clustering and Embedding with Hidden Markov Models -- Probabilistic Explanation Based Learning -- Graph-Based Domain Mapping for Transfer Learning in General Games -- Learning to Classify Documents with Only a Small Positive Training Set -- Structure Learning of Probabilistic Relational Models from Incomplete Relational Data -- Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA -- Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures -- Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs -- Source Separation with Gaussian Process Models -- Discriminative Sequence Labeling by Z-Score Optimization -- Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches -- Bayesian Inference for Sparse Generalized Linear Models -- Classifier Loss Under Metric Uncertainty -- Additive Groves of Regression Trees -- Efficient Computation of Recursive Principal Component Analysis for Structured Input -- Hinge Rank Loss and the Area Under the ROC Curve -- Clustering Trees with Instance Level Constraints -- On Pairwise Naive Bayes Classifiers -- Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models -- Safe Q-Learning on Complete History Spaces -- Random k-Labelsets: An Ensemble Method for Multilabel Classification -- Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble -- Avoiding Boosting Overfitting by Removing Confusing Samples -- Planning and Learning in Environments with Delayed Feedback -- Analyzing Co-training Style Algorithms -- Policy Gradient Critics -- An Improved Model Selection Heuristic for AUC -- Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators -- Short Papers -- Stepwise Induction of Multi-target Model Trees -- Comparing Rule Measures for Predictive Association Rules -- User Oriented Hierarchical Information Organization and Retrieval -- Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition -- Weighted Kernel Regression for Predicting Changing Dependencies -- Counter-Example Generation-Based One-Class Classification -- Test-Cost Sensitive Classification Based on Conditioned Loss Functions -- Probabilistic Models for Action-Based Chinese Dependency Parsing -- Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search -- A Simple Lexicographic Ranker and Probability Estimator -- On Minimizing the Position Error in Label Ranking -- On Phase Transitions in Learning Sparse Networks -- Semi-supervised Collaborative Text Classification -- Learning from Relevant Tasks Only -- An Unsupervised Learning Algorithm for Rank Aggregation -- Ensembles of Multi-Objective Decision Trees -- Kernel-Based Grouping of Histogram Data -- Active Class Selection -- Sequence Labeling with Reinforcement Learning and Ranking Algorithms -- Efficient Pairwise Classification -- Scale-Space Based Weak Regressors for Boosting -- K-Means with Large and Noisy Constraint Sets -- Towards ‘Interactive’ Active Learning in Multi-view Feature Sets for Information Extraction -- Principal Component Analysis for Large Scale Problems with Lots of Missing Values -- Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling -- Class Noise Mitigation Through Instance Weighting -- Optimizing Feature Sets for Structured Data -- Roulette Sampling for Cost-Sensitive Learning -- Modeling Highway Traffic Volumes -- Undercomplete Blind Subspace Deconvolution Via Linear Prediction -- Learning an Outlier-Robust Kalman Filter -- Imitation Learning Using Graphical Models -- Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks -- Semi-definite Manifold Alignment -- General Solution for Supervised Graph Embedding -- Multi-objective Genetic Programming for Multiple Instance Learning -- Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning. |
| Altri titoli varianti |
European Conference on Machine Learning
ECML 2007 18th European Conference on Machine Learning Eighteenth European Conference on Machine Learning |
| Record Nr. | UNINA-9910485018703321 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning : ECML 2007 : 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 : proceedings / / edited by Joost N. Kok [and four others]
| Machine learning : ECML 2007 : 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 : proceedings / / edited by Joost N. Kok [and four others] |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (XXIV, 812 p.) |
| Disciplina | 006.31 |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence ;4701 |
| Soggetto topico | Machine learning |
| ISBN | 3-540-74958-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Learning, Information Extraction and the Web -- Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation -- Mining Queries -- Adventures in Personalized Information Access -- Long Papers -- Statistical Debugging Using Latent Topic Models -- Learning Balls of Strings with Correction Queries -- Neighborhood-Based Local Sensitivity -- Approximating Gaussian Processes with -Matrices -- Learning Metrics Between Tree Structured Data: Application to Image Recognition -- Shrinkage Estimator for Bayesian Network Parameters -- Level Learning Set: A Novel Classifier Based on Active Contour Models -- Learning Partially Observable Markov Models from First Passage Times -- Context Sensitive Paraphrasing with a Global Unsupervised Classifier -- Dual Strategy Active Learning -- Decision Tree Instability and Active Learning -- Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering -- The Cost of Learning Directed Cuts -- Spectral Clustering and Embedding with Hidden Markov Models -- Probabilistic Explanation Based Learning -- Graph-Based Domain Mapping for Transfer Learning in General Games -- Learning to Classify Documents with Only a Small Positive Training Set -- Structure Learning of Probabilistic Relational Models from Incomplete Relational Data -- Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA -- Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures -- Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs -- Source Separation with Gaussian Process Models -- Discriminative Sequence Labeling by Z-Score Optimization -- Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches -- Bayesian Inference for Sparse Generalized Linear Models -- Classifier Loss Under Metric Uncertainty -- Additive Groves of Regression Trees -- Efficient Computation of Recursive Principal Component Analysis for Structured Input -- Hinge Rank Loss and the Area Under the ROC Curve -- Clustering Trees with Instance Level Constraints -- On Pairwise Naive Bayes Classifiers -- Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models -- Safe Q-Learning on Complete History Spaces -- Random k-Labelsets: An Ensemble Method for Multilabel Classification -- Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble -- Avoiding Boosting Overfitting by Removing Confusing Samples -- Planning and Learning in Environments with Delayed Feedback -- Analyzing Co-training Style Algorithms -- Policy Gradient Critics -- An Improved Model Selection Heuristic for AUC -- Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators -- Short Papers -- Stepwise Induction of Multi-target Model Trees -- Comparing Rule Measures for Predictive Association Rules -- User Oriented Hierarchical Information Organization and Retrieval -- Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition -- Weighted Kernel Regression for Predicting Changing Dependencies -- Counter-Example Generation-Based One-Class Classification -- Test-Cost Sensitive Classification Based on Conditioned Loss Functions -- Probabilistic Models for Action-Based Chinese Dependency Parsing -- Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search -- A Simple Lexicographic Ranker and Probability Estimator -- On Minimizing the Position Error in Label Ranking -- On Phase Transitions in Learning Sparse Networks -- Semi-supervised Collaborative Text Classification -- Learning from Relevant Tasks Only -- An Unsupervised Learning Algorithm for Rank Aggregation -- Ensembles of Multi-Objective Decision Trees -- Kernel-Based Grouping of Histogram Data -- Active Class Selection -- Sequence Labeling with Reinforcement Learning and Ranking Algorithms -- Efficient Pairwise Classification -- Scale-Space Based Weak Regressors for Boosting -- K-Means with Large and Noisy Constraint Sets -- Towards ‘Interactive’ Active Learning in Multi-view Feature Sets for Information Extraction -- Principal Component Analysis for Large Scale Problems with Lots of Missing Values -- Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling -- Class Noise Mitigation Through Instance Weighting -- Optimizing Feature Sets for Structured Data -- Roulette Sampling for Cost-Sensitive Learning -- Modeling Highway Traffic Volumes -- Undercomplete Blind Subspace Deconvolution Via Linear Prediction -- Learning an Outlier-Robust Kalman Filter -- Imitation Learning Using Graphical Models -- Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks -- Semi-definite Manifold Alignment -- General Solution for Supervised Graph Embedding -- Multi-objective Genetic Programming for Multiple Instance Learning -- Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning. |
| Altri titoli varianti |
European Conference on Machine Learning
ECML 2007 18th European Conference on Machine Learning Eighteenth European Conference on Machine Learning |
| Record Nr. | UNISA-996465580103316 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine learning : ECML 2006 : 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006 : proceedings / / Johannes Furnkranz, Tobias Scheffer, Myra Spiliopoulou (eds.)
| Machine learning : ECML 2006 : 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006 : proceedings / / Johannes Furnkranz, Tobias Scheffer, Myra Spiliopoulou (eds.) |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, c2006 |
| Descrizione fisica | 1 online resource (XXIII, 851 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) |
FurnkranzJohannes
SchefferTobias SpiliopoulouMyra |
| Collana |
Lecture notes in computer science. Lecture notes in artificial intelligence
LNCS sublibrary. SL 7, Artificial intelligence |
| Soggetto topico | Machine learning |
| ISBN | 3-540-46056-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- On Temporal Evolution in Data Streams -- The Future of CiteSeer: CiteSeerx -- Learning to Have Fun -- Winning the DARPA Grand Challenge -- Challenges of Urban Sensing -- Long Papers -- Learning in One-Shot Strategic Form Games -- A Selective Sampling Strategy for Label Ranking -- Combinatorial Markov Random Fields -- Learning Stochastic Tree Edit Distance -- Pertinent Background Knowledge for Learning Protein Grammars -- Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies -- Sequence Discrimination Using Phase-Type Distributions -- Languages as Hyperplanes: Grammatical Inference with String Kernels -- Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning -- Fisher Kernels for Relational Data -- Evaluating Misclassifications in Imbalanced Data -- Improving Control-Knowledge Acquisition for Planning by Active Learning -- PAC-Learning of Markov Models with Hidden State -- A Discriminative Approach for the Retrieval of Images from Text Queries -- TildeCRF: Conditional Random Fields for Logical Sequences -- Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data -- Bayesian Learning of Markov Network Structure -- Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks -- Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions -- EM Algorithm for Symmetric Causal Independence Models -- Deconvolutive Clustering of Markov States -- Patching Approximate Solutions in Reinforcement Learning -- Fast Variational Inference for Gaussian Process Models Through KL-Correction -- Bandit Based Monte-Carlo Planning -- Bayesian Learning with Mixtures of Trees -- Transductive Gaussian Process Regression with Automatic Model Selection -- Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees -- Why Is Rule Learning Optimistic and How to Correct It -- Automatically Evolving Rule Induction Algorithms -- Bayesian Active Learning for Sensitivity Analysis -- Mixtures of Kikuchi Approximations -- Boosting in PN Spaces -- Prioritizing Point-Based POMDP Solvers -- Graph Based Semi-supervised Learning with Sharper Edges -- Margin-Based Active Learning for Structured Output Spaces -- Skill Acquisition Via Transfer Learning and Advice Taking -- Constant Rate Approximate Maximum Margin Algorithms -- Batch Classification with Applications in Computer Aided Diagnosis -- Improving the Ranking Performance of Decision Trees -- Multiple-Instance Learning Via Random Walk -- Localized Alternative Cluster Ensembles for Collaborative Structuring -- Distributional Features for Text Categorization -- Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data -- An Adaptive Kernel Method for Semi-supervised Clustering -- To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles -- Ensembles of Nearest Neighbor Forecasts -- Short Papers -- Learning Process Models with Missing Data -- Case-Based Label Ranking -- Cascade Evaluation of Clustering Algorithms -- Making Good Probability Estimates for Regression -- Fast Spectral Clustering of Data Using Sequential Matrix Compression -- An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects -- Efficient Inference in Large Conditional Random Fields -- A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses -- Cost-Sensitive Decision Tree Learning for Forensic Classification -- The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces -- Right of Inference: Nearest Rectangle Learning Revisited -- Reinforcement Learning for MDPs with Constraints -- Efficient Non-linear Control Through Neuroevolution -- Efficient Prediction-Based Validation for Document Clustering -- On Testing the Missing at Random Assumption -- B-Matching for Spectral Clustering -- Multi-class Ensemble-Based Active Learning -- Active Learning with Irrelevant Examples -- Classification with Support Hyperplanes -- (Agnostic) PAC Learning Concepts in Higher-Order Logic -- Evaluating Feature Selection for SVMs in High Dimensions -- Revisiting Fisher Kernels for Document Similarities -- Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery -- Robust Probabilistic Calibration -- Missing Data in Kernel PCA -- Exploiting Extremely Rare Features in Text Categorization -- Efficient Large Scale Linear Programming Support Vector Machines -- An Efficient Approximation to Lookahead in Relational Learners -- Improvement of Systems Management Policies Using Hybrid Reinforcement Learning -- Diversified SVM Ensembles for Large Data Sets -- Dynamic Integration with Random Forests -- Bagging Using Statistical Queries -- Guiding the Search in the NO Region of the Phase Transition Problem with a Partial Subsumption Test -- Spline Embedding for Nonlinear Dimensionality Reduction -- Cost-Sensitive Learning of SVM for Ranking -- Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures. |
| Altri titoli varianti |
ECML 2006
17th European Conference on Machine Learning Seventeenth European Conference on Machine Learning European Conference on Machine Learning |
| Record Nr. | UNINA-9910768437803321 |
| Berlin ; ; New York, : Springer, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||