Machine Learning: ECML 2003

14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings

Häftad, Engelska, 2003

Av Nada Lavrač, Dragan Gamberger, Ljupco Todorovski, Hendrik Blockeel, Nada Lavrač, Nada Lavrac, Nada Lavra¿

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This book constitutes the refereed proceedings of the 14th European Conference on Machine Learning, ECML 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with PKDD 2003.The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for PKDD 2003, selected from a total of 332 submissions. The papers address all current issues in machine learning including support vector machine, inductive inference, feature selection algorithms, reinforcement learning, preference learning, probabilistic grammatical inference, decision tree learning, clustering, classification, agent learning, Markov networks, boosting, statistical parsing, Bayesian learning, supervised learning, and multi-instance learning.

Produktinformation

Utgivningsdatum2003-09-12
Mått155 x 235 x 29 mm
Vikt791 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor512
Upplaga2003
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540201212

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Invited Papers.- From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challenge.- Two-Eyed Algorithms and Problems.- Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditional Data.- Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions.- Contributed Papers.- Support Vector Machines with Example Dependent Costs.- Abalearn: A Risk-Sensitive Approach to Self-play Learning in Abalone.- Life Cycle Modeling of News Events Using Aging Theory.- Unambiguous Automata Inference by Means of State-Merging Methods.- Could Active Perception Aid Navigation of Partially Observable Grid Worlds?.- Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language.- Iteratively Extending Time Horizon Reinforcement Learning.- Volume under the ROC Surface for Multi-class Problems.- Improving the AUC of Probabilistic Estimation Trees.- Scaled CGEM: AFast Accelerated EM.- Pairwise Preference Learning and Ranking.- A New Way to Introduce Knowledge into Reinforcement Learning.- Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference.- COllective INtelligence with Sequences of Actions.- Rademacher Penalization over Decision Tree Prunings.- Learning Rules to Improve a Machine Translation System.- Optimising Performance of Competing Search Engines in Heterogeneous Web Environments.- Robust k-DNF Learning via Inductive Belief Merging.- Logistic Model Trees.- Color Image Segmentation: Kernel Do the Feature Space.- Evaluation of Topographic Clustering and Its Kernelization.- A New Pairwise Ensemble Approach for Text Classification.- Self-evaluated Learning Agent in Multiple State Games.- Classification Approach towards Ranking and Sorting Problems.- Using MDP Characteristics to Guide Exploration in Reinforcement Learning.- Experiments with Cost-Sensitive Feature Evaluation.- A Markov Network Based Factorized Distribution Algorithm for Optimization.- On Boosting Improvement: Error Reduction and Convergence Speed-Up.- Improving SVM Text Classification Performance through Threshold Adjustment.- Backoff Parameter Estimation for the DOP Model.- Improving Numerical Prediction with Qualitative Constraints.- A Generative Model for Semantic Role Labeling.- Optimizing Local Probability Models for Statistical Parsing.- Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-agent Systems.- Visualizations for Assessing Convergence and Mixing of MCMC.- A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes.- Improving Rocchio with Weakly Supervised Clustering.- A Two-Level Learning Method for Generalized Multi-instance Problems.- Clustering in Knowledge Embedded Space.- Ensembles of Multi-instance Learners.