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Machine Learning: Proceedings of the Tenth International Conference covers the papers presented at the Tenth International Conference on Machine Learning, held at Amherst, Massachusetts in June 27-29, 1993. The book focuses on the advancements of techniques, practices, approaches, and methodologies in machine learning.
The selection first offers information on automatic algorithm/model class selection, using decision trees to improve case-based learning, GALOIS, and multitask learning. Discussions focus on multitask connectionist learning in more detail; multitask decision trees; an algorithm for the incremental determination of the concept lattice; and empirical evaluation of GALOIS as a learning system. The text then examines the use of qualitative models to guide inductive learning; automation of path analysis for building causal models from data; and construction of hidden variables in Bayesian networks via conceptual clustering.
The book ponders on synthesis of abstraction hierarchies for constraint satisfaction by clustering approximately equivalent objects; efficient domain-independent experimentation; learning search control knowledge for deep space network scheduling; and learning procedures from interactive natural language instructions.
The selection is a dependable reference for researchers wanting to explore the field of machine learning.
Organizing Committee and Program Committee
The Evolution of Genetic Algorithms: Towards Massive Parallelism
ÉLÉNA: A Bottom-Up Learning Method
Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection
Using Decision Trees to Improve Case-Based Learning
GALOIS: An Order-Theoretic Approach to Conceptual Clustering
Multitask Learning: A Knowledge-Based Source of Inductive Bias
Using Qualitative Models to Guide Inductive Learning
Automating Path Analysis for Building Causal Models from Data
Constructing Hidden Variables in Bayesian Networks Via Conceptual Clustering
Learning Symbolic Rules Using Artificial Neural Networks
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network
Concept Sharing: A Means to Improve Multi-Concept Learning
Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects
SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys
Learning From Entailment: An Application to Propositional Horn Sentences
Efficient Domain-Independent Experimentation
Learning Search Control Knowledge for Deep Space Network Scheduling
Learning Procedures from Interactive Natural Language Instructions
Generalization Under Implication by Recursive Anti-Unification
Supervised Learning and Divide-and-Conquer: A Statistical Approach
Hierarchical Learning in Stochastic Domains: Preliminary Results
Constraining Learning with Search Control
Scaling Up Reinforcement Learning for Robot Control
Overcoming Incomplete Perception with Utile Distinction Memory
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches
Combinatorial Optimization in Inductive Concept Learning
Decision Theoretic Subsampling for Induction on Large Databases
Learning DNF Via Probabilistic Evidence Combination
Explaining and Generalizing Diagnostic Decisions
Combining Instance-Based and Model-Based Learning
Data Mining of Subjective Agricultural Data
Lookahead Feature Construction for Learning Hard Concepts
Adaptive NeuroControl: How Black Box and Simple can it be
An SE-Tree Based Characterization of the Induction Problem
Density-Adaptive Learning and Forgetting
Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning
Compiling Bayesian Networks into Neural Networks
A Reinforcement Learning Method for Maximizing Undiscounted Rewards
ATM Scheduling with Queuing Delay Predictions
Online Learning with Random Representations
Learning from Queries and Examples with Tree-Structured Bias
Multi-Agent Reinforcement Learning: Independent Vs. Cooperative Agents
Better Learners Use Analogical Problem Solving Sparingly
- No. of pages:
- © Morgan Kaufmann 1993
- 1st July 1993
- Morgan Kaufmann
- eBook ISBN:
Dr. Blessy Joseph is a Research Fellow at the International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India. Her research has focused on green synthesis of nanoparticles for biomedical applications, and the development of polymer scaffolds for biomedical applications. Joseph has contributed several book chapters in related areas.
Dr. Blessy Joseph is a Research Fellow at the International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India.
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