Machine Learning: ECML 2003: 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings: Lecture Notes in Computer Science, cartea 2837
Editat de Nada Lavrač, Dragan Gamberger, Ljupco Todorovski, Hendrik Blockeelen Limba Engleză Paperback – 12 sep 2003
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Specificații
ISBN-13: 9783540201212
ISBN-10: 3540201211
Pagini: 528
Ilustrații: XVI, 512 p.
Dimensiuni: 155 x 235 x 28 mm
Greutate: 0.77 kg
Ediția:2003
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540201211
Pagini: 528
Ilustrații: XVI, 512 p.
Dimensiuni: 155 x 235 x 28 mm
Greutate: 0.77 kg
Ediția:2003
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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.
Caracteristici
Includes supplementary material: sn.pub/extras