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Machine Learning Proceedings 1991
Proceedings of the Eighth International Workshop (ML91)
- 1st Edition - June 1, 1991
- Editors: Lawrence A. Birnbaum, Gregg C. Collins
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 2 0 0 - 7
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 9 8 1 7 - 7
Machine Learning: Proceedings of the Eighth International Workshop (ML91) covers the papers presented at ML91, the Eighth International Workshop on Machine Learning, held at… Read more
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Request a sales quoteMachine Learning: Proceedings of the Eighth International Workshop (ML91) covers the papers presented at ML91, the Eighth International Workshop on Machine Learning, held at Northwestern University, Evanston, Illinois, USA, in June 1991. The book focuses on constructive induction, learning from theory and data, automated knowledge acquisition, learning in intelligent information retrieval, machine learning in engineering automation, computational models of human learning, and learning reaction strategies. The selection first offers information on design rationale capture as knowledge acquisition, a domain-independent framework for effective experimentation in planning, and knowledge refinement using a high-level, non-technical vocabulary. The text then elaborates on improving the performance of inconsistent knowledge bases via combined optimization method, flexibility of speculative refinement, and a prototype based symbolic concept learning system. Topics include using task descriptions to generate error candidates, functional descriptions of knowledge-based systems, combined optimization method, and inconsistency and related work. The book ponders on learning words from context, modeling the acquisition and improvement of motor skills, a computational model of acquisition for children's addition strategies, and computer modeling of acquisition orders in child language. The manuscript also takes a look at knowledge acquisition combining analytical and empirical techniques; designing integrated learning systems for engineering design; and machine learning for nondestructive evaluation. The selection is highly recommended for researchers interested in machine learning.
Part I Automated Knowledge Acquisition
Design Rationale Capture as Knowledge Acquisition
A Domain-Independent Framework for Effective Experimentation in Planning
Knowledge Refinement Using a High Level, Non-Technical Vocabulary
Improving the Performance of Inconsistent Knowledge Bases Via Combined Optimization Method
The Flexibility of Speculative Refinement
Generating Error Candidates for Assigning Blame in a Knowledge Base
Part II Computational Models of Human Learning
A Prototype Based Symbolic Concept Learning System
Combining Evidence of Deep and Surface Similarity
The Importance of Causal Structure and Facts in Evaluating Explanations
Learning Words From Context
Modeling the Acquisition and Improvement of Motor Skills
A Computational Model of Acquisition for Children's Addition Strategies
Internal World Models and Supervised Learning
Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic Acquisition
The Acquisition of Human Planning Expertise
Adaptive Pattern-Oriented Chess
Variability Bias and Category Learning
A Constraint-Motivated Model of Lexical Acquisition
Computer Modeling of Acquisition Orders in Child Language
Simulating Stages of Human Cognitive Development With Connectionist Models
Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control
Part III Constructive Induction
Incremental Constructive Induction: An Instance-Based Approach
A Transformational Approach to Constructive Induction
Learning Variable Descriptors for Applying Heuristics Across CSP Problems
Informed Pruning in Constructive Induction
A Hybrid Method for Feature Generation
Abstracting Concepts with Inverse Resolution
Opportunistic Constructive Induction
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning
Discovering Production Rules with Higher Order Neural Networks
Constructive Induction on Symbolic Features
Comparison of Methods Based on Inverse Resolution
The Need for Constructive Induction
Constructive Induction in Theory Refinement
Constructive Induction of M-of-N Terms
Relations, Knowledge and Empirical Learning
Learning Concepts by Synthesizing Minimal Threshold Gate Networks
On the Effect of Instance Representation on Generalization
Relational Clichés: Constraining Constructive Induction During Relational Learning
Learning Polynomial Functions by Feature Construction
Constructive Induction in Knowledge-Based Neural Networks
Feature Construction in Structural Decision Trees
Fringe-Like Feature Construction: A Comparative Study and a Unifying Scheme
A Neural Network Approach to Constructive Induction
Part IV Learning in Intelligent Information Retrieval
Learning in Intelligent Information Retrieval
A Probabilistic Retrieval Scheme for Cluster-Based Adaptive Information Retrieval
Classification Trees for Information Retrieval
Query Formulation Through Knowledge Acquisition
Incremental Learning in a Probabilistic Information Retrieval System
Query Learning Using an ANN with Adaptive Architecture
A Goal-Based Approach to Intelligent Information Retrieval
Machine Learning in the Combination of Expert Opinion Approach to IR
Predicting Actions from Induction on Past Performance
Part V Learning Reaction Strategies
Decision-Theoretic Learning in an Action System
On Becoming Decreasingly Reactive: Learning to Deliberate Minimally
Learning the Persistence of Actions in Reactive Control Rules
Learning to Avoid Obstacles Through Reinforcement
Learning Footfall Evaluation for a Walking Robot
The Blind Leading the Blind: Mutual Refinement of Approximate Theories
Learning to Select a Model in a Changing World
Learning from Deliberated Reactivity
Self-Improvement Based on Reinforcement Learning, Planning and Teaching
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture
Variable Resolution Dynamic Programming
Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus
Incremental Development of Complex Behaviors
Transfer of Learning Across Compositions of Sequential Tasks
Planning by Incremental Dynamic Programming
Learning a Cost-Sensitive Internal Representation for Reinforcement Learning
Complexity and Cooperation in Q-Learning
Scaling Reinforcement Learning Techniques via Modularity
Part VI Learning Relations
Probabilistic Concept Formation in Relational Domains
Experiments in Non-Monotonic Learning
Learning Qualitative Models of Dynamic Systems
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms
Integrity Constraints and Interactive Concept-Learning
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL
Inducing Temporal Fault Diagnostic Rules from a Qualitative Model
Learning Spatial Relations from Images
Using Inverse Resolution to Learn Relations from Experiments
Efficient Learning of Logic Programs with Non-Determinant, Non-Discriminating Literals
Learning Search Control Rules for Planning: An Inductive Approach
Learning Constrained Atoms
A Knowledge-Intensive Approach to Learning Relational Concepts
The Consistent Concept Axiom
Determinate Literals in Inductive Logic Programming
First-Order Theory Revision
Completeness for Inductive Procedures
Constraints on Predicate Invention
Revising Relational Domain Theories
Learning Stochastic Motifs from Genetic Sequences
Part VII Learning From Theory and Data
Refinement of Approximate Reasoning-Based Controllers by Reinforcement Learning
Improving Learning Using Causality and Abduction
The DUCTOR: A Theory Revision System for Propositional Domains
The Generality of Overgenerality
Probabilistic Evaluation of Bias for Learning Systems
Incremental Refinement of Approximate Domain Theories
An Enhancer for Reactive Plans
A Hybrid Approach to Guaranteed Effective Control Strategies
Revision Cost for Theory Refinement
Revision of Reduced Theories
Refining Domain Theories Expressed as Finite-State Automata
A Smallest Generalization Step Strategy
Improving Shared Rules in Multiple Category Domain Theories
Discovering Regularities from Large Knowledge Bases
Learning with Inscrutable Theories
A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications
Using Background Knowledge in Concept Formation
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems
Is it a Pocket or a Purse? Tightly Coupled Theory and Data Driven Learning
Identifying Cost Effective Boundaries of Operationally
Part VIII Machine Learning in Engineering Automation
Machine Learning In Engineering Automation
Noise-Resistant Classification
Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans
Conceptual Clustering and Exploratory Data Analysis
Megainduction: a Test Flight
Knowledge Compilation to Speed Up Numerical Optimization
Model Revision: A Theory of Incremental Model Learning
Learning Analytical Knowledge About VLSI-Design from Observation
Continuous Conceptual Set Covering: Learning Robot Operators From Examples
Machine Learning for Nondestructive Evaluation
Improving Recognition Effectiveness of Noisy Texture Concepts
Knowledge-Based Equation Discovery in Engineering Domains
Designing Integrated Learning Systems for Engineering Design
Database Consistency Via Inductive Learning
AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision Making
Decision Tree Induction of 3-D Manufacturing Features
Part IX Addendum
Knowledge Acquisition Combining Analytical and Empirical Techniques
- No. of pages: 661
- Language: English
- Edition: 1
- Published: June 1, 1991
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9781558602007
- eBook ISBN: 9781483298177