On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations
Vilalta, Ricardo
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https://hdl.handle.net/2142/81921
Description
Title
On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations
Author(s)
Vilalta, Ricardo
Issue Date
1998
Doctoral Committee Chair(s)
Larry Rendell
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
HCL achieves two functionalities: (1) flexibility in the representation, by increasing the complexity of the hypothesis comprised at each hierarchical layer, and (2) adaptability in the search for different representations, to know when to stop adding more layers in top of the hierarchical structure. Adaptability allows HCL to adjust the complexity of the representation by building few hierarchical levels when the concept is simple, and by increasing the number of levels as the difficulty of the concept grows higher. HCL is assessed experimentally using both artificial and real-world domains. Results show how HCL outperforms other models significantly when many intermediate concepts lie between the primitive features and the target concept. (Abstract shortened by UMI.).
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