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Machine Learning Proceedings 1995
Fast Effective Rule Induction
edited_book
Author(s):
William W. Cohen
Publication date
(Print):
1995
Publisher:
Elsevier
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There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
Related collections
Recursive Rule based Visual Categorization
Author and book information
Book Chapter
Publication date (Print):
1995
Pages
: 115-123
DOI:
10.1016/B978-1-55860-377-6.50023-2
SO-VID:
65897f98-37f5-4979-b291-5555db584ba6
License:
https://www.elsevier.com/tdm/userlicense/1.0/
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Book chapters
pp. 21
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
pp. 30
Residual Algorithms: Reinforcement Learning with Function Approximation
pp. 38
Removing the Genetics from the Standard Genetic Algorithm
pp. 90
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data
pp. 108
K*: An Instance-based Learner Using an Entropic Distance Measure
pp. 115
Fast Effective Rule Induction
pp. 124
Text Categorization and Relational Learning
pp. 150
Committee-Based Sampling For Training Probabilistic Classifiers
pp. 194
Supervised and Unsupervised Discretization of Continuous Features
pp. 252
Ant-Q: A Reinforcement Learning approach to the traveling salesman problem
pp. 261
Stable Function Approximation in Dynamic Programming
pp. 295
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
pp. 304
Automatic Parameter Selection by Minimizing Estimated Error
pp. 313
Error-Correcting Output Coding Corrects Bias and Variance
pp. 331
NewsWeeder: Learning to Filter Netnews
pp. 362
Learning policies for partially observable environments: Scaling up
pp. 387
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State
pp. 456
Compression-Based Discretization of Continuous Attributes
pp. 471
For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance
pp. 531
TD Models: Modeling the World at a Mixture of Time Scales
pp. 558
Learning with Rare Cases and Small Disjuncts
pp. 588
Learning With Bayesian Networks
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