Machine Learning Proceedings 1994 - 1st Edition - ISBN: 9781558603356, 9781483298184

Machine Learning Proceedings 1994

1st Edition

Proceedings of the Eighth International Conference

Editors: William W. Cohen
eBook ISBN: 9781483298184
Imprint: Morgan Kaufmann
Published Date: 1st July 1994
Page Count: 381
Tax/VAT will be calculated at check-out Price includes VAT (GST)
Price includes VAT (GST)
× DRM-Free

Easy - Download and start reading immediately. There’s no activation process to access eBooks; all eBooks are fully searchable, and enabled for copying, pasting, and printing.

Flexible - Read on multiple operating systems and devices. Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle.

Open - Buy once, receive and download all available eBook formats, including PDF, EPUB, and Mobi (for Kindle).

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


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.

Table of Contents




Organizing Committee

Program Committee


Contributed Papers

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

Invited Talks

A Statistical Approach to Decision Tree Modeling

Bayesian Inductive Logic Programming

Frequencies Vs Biases: Machine Learning Problems in Natural Language Processing—Abstract

Author Index


No. of pages:
© Morgan Kaufmann 1994
Morgan Kaufmann
eBook ISBN:

About the Editor

William W. Cohen