Use of Domain Knowledge in Constructive Induction
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Summary
This thesis proposal considers the representation problem for a class of architectures in which the problem-solver is a search procedure and the inductive learning algorithm is a source of heuristic guidance and presents a solution that results in faster and more accurate learning.
- Type
- article
- Published
- 1990-01-01
- Cited by
- 5
- References
- 53
- OpenAlex
- https://openalex.org/W29537492
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:18849991
Keywords
Domain (mathematical analysis), Constructive, Computer science, Mathematics, Process (computing)
References
- Substantial Constructive Induction Using Layered Information Compression: Tractable Feature Formation in Search
- Perceptron Trees: A Case Study In Hybrid Concept Representations
- A Method for Computing Heuristics in Problem-Solving
- Why AM and EURISKO Appear to Work
- Learning DNF by Decision Trees
- Selecting Appropriate Representations for Learning from Examples
- Acquisition of Appropriate Bias for Inductive Concept Learning
- Learning Operator Transformations
- Feature Discovery for Inductive Concept Learning
- Constructive Induction On Decision Trees
- Principled Constructive Induction
- Feature Discovery by Competive Learning
- Learning Intermediate Concepts in Constructing a Hierarchical Knowledge Base
- Change of Representation and Inductive Bias
- Shift of bias for inductive concept learning
- Heuristics : intelligent search strategies for computer problem solving
- Discovering Admissible Heuristics by Abstracting and Optimizing: A Transformational Approach
- A Theory and Methodology of Inductive Learning
- Dynamic node creation in backpropagation networks
- A Result on the Computational Complexity of Heuristic Estimates for the A* Algorithm
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