Machine learning: proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12, 1995 |
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Contents
On Handling TreeStructured Attributes in Decision Tree Learning | 12 |
Reinforcement Learning with Function Approximation | 30 |
Inductive Learning of Reactive Action Models | 47 |
Copyright | |
32 other sections not shown
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Common terms and phrases
action agent Ant-Q applied approach Artificial Intelligence attributes average Bayesian Bayesian network Bellman Bellman equation bias binary bound classifier complexity concept converge cross-validation datasets decision tree defined described discretization distribution domain domain theory dynamic programming ECOC encoding Equation error rate estimate evaluation experiments Figure Genetic Algorithms Gittins Gittins index given grid heuristic hierarchy hypothesis induction initial input instances interval IREP iteration label learner learning algorithm lexical linear Machine Learning method Morgan Kaufmann neural networks nodes noise operator optimal output paper parameters partition performance predictive accuracy prob probability probability vector problem pruning Q-learning query Quinlan random reinforcement learning relation reward rithm rules sample selection sequence simulated solution space speaker recognition split strategy subset synsets Table task techniques test set tf-idf Theorem theory tion training data training examples training set trial update variables vector weight Winnow
Bibliographic information
| Title | Machine learning: proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12, 1995 The Morgan Kaufmann series in machine learning |
| Author | Armand Prieditis |
| Editors | Armand Prieditis, Stuart Jonathan Russell |
| Publisher | Morgan Kaufmann Publishers, c1995., 1995 |
| Original from | Cornell University |
| Digitized | Sep 10, 2010 |
| ISBN | 1558603778, 9781558603776 |
| Length | 591 pages |
| Subjects | › Computers / Artificial Intelligence Computers / Intelligence (AI) & Semantics Computers / Machine Theory Machine learning |
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