On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations

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.).

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Vilalta, Ricardo

Contributors dc:contributor

• Larry Rendell

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI9904611

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81921