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Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees.
The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms.
The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees.
The selection is a dependable source of data for researchers interested in machine learning.
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars
Learning Recursive Relations with Randomly Selected Small Training Sets
Improving Accuracy of Incorrect Domain Theories
Greedy Attribute Selection
Using Sampling and Queries to Extract Rules from Trained Neural Networks
The Generate, Test, and Explain Discovery System Architecture
Boosting and Other Machine Learning Algorithms
In Defense of C4.5: Notes on Learning One-Level Decision Trees
Incremental Reduced Error Pruning
An Incremental Learning Approach for Completable Planning
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains
Learning Disjunctive Concepts by Means of Genetic Algorithms
Consideration of Risk in Reinforcement Learning
Rule Induction for Semantic Query Optimization
Irrelevant Features and the Subset Selection Problem
An Efficient Subsumption Algorithm for Inductive Logic Programming
Getting the Most from Flawed Theories
Heterogeneous Uncertainty Sampling for Supervised Learning
Markov Games as a Framework for Multi-Agent Reinforcement Learning
To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning
Comparing Methods for Refining Certainty-Factor Rule-Bases
Reward Functions for Accelerated Learning
Efficient Algorithms for Minimizing Cross Validation Error
Revision of Production System Rule-Bases
Using Genetic Search to Refine Knowledge-Based Neural Networks
Reducing Misclassification Costs
Incremental Multi-Step Q-Learning
The Minimum Description Length Principle and Categorical Theories
Towards a Better Understanding of Memory-Based Reasoning Systems
Hierarchical Self-Organization in Genetic Programming
A Conservation Law for Generalization Performance
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms
A Constraint-Based Induction Algorithm in FOL
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
A Baysian Framework to Integrate Symbolic and Neural Learning
A Modular Q-Learning Architecture for Manipulator Task Decomposition
An Improved Algorithm for Incremental Induction of Decision Trees
A Powerful Heuristic for the Discovery of Complex Patterned Behavior
Small Sample Decision Tree Pruning
Combining Top-Down and Bottom-Up Techniques in Inductive Logic Programming
Selective Reformulation of Examples in Concept Learning
A Statistical Approach to Decision Tree Modeling
Bayesian Inductive Logic Programming
Frequencies Vs Biases: Machine Learning Problems in Natural Language Processing—Abstract
- No. of pages:
- © Morgan Kaufmann 1994
- 1st July 1994
- Morgan Kaufmann
- eBook ISBN:
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