Machine Learning: ECML 2002 - neues Buch

EAN (ISBN-13): 9783540367550
Erscheinungsjahr: 2002
Herausgeber: Springer Berlin Heidelberg

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ISBN/EAN: 9783540367550

ISBN - alternative Schreibweisen:
978-3-540-36755-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: berlin, toivonen, tapio, castegnaro
Titel des Buches: machine learning, helsinki, finland

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Daten vom Verlag:

Autor/in: Tapio Elomaa; Heikki Mannila; Hannu Toivonen
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Machine Learning: ECML 2002 - 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings
Verlag: Springer; Springer Berlin
538 Seiten
Erscheinungsjahr: 2002-01-01
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
XIV, 538 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Boosting; Markov decision process; Support Vector Machine; classification; kernel method; learning; machine learning; reinforcement learning; algorithm analysis and problem complexity; C; Artificial Intelligence; Algorithms; Formal Languages and Automata Theory; Computer Science; Algorithmen und Datenstrukturen; Theoretische Informatik; BC

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.
Includes supplementary material: sn.pub/extras