Change of representation in machine learning, and an application to protein structure prediction

While many excellent induction algorithms are known for making predictions from databases in well-studied domains, learning systems still perform poorly in many difficult real-world domains, such as weather prediction or financial risk analysis. Two characteristics of real-world domains are inadequately addressed by current machine learning research. First, the difficulty in these domains is often caused by a low-level representation, which necessitates shifting to a higher-level representation. But the space of possible representations is very large, so we need intelligent methods for finding higher-level representations. Second, background knowledge is almost always available in real-world domains, which we would like to take advantage of to increase predictive accuracy. However, known roles for domain knowledge in machine learning are often inflexible, requiring the use of a specific induction algorithm or being sensitive to incorrectness or incompleteness in the knowledge.

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

2011

Author dc:creator

• Ioerger, Thomas Richard

Contributors dc:contributor

• Rendell, Larry A.

• Chemistry, Physical
• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1996 Ioerger, Thomas Richard

Language dc:language

eng

Identifier

9780591199116
AAI9712321
(UMI)AAI9712321

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/21110