A probabilistic reasoning-based approach to machine learning
Purswani, Krishnakumar Sukhramdas
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Permalink
https://hdl.handle.net/2142/19623
Description
Title
A probabilistic reasoning-based approach to machine learning
Author(s)
Purswani, Krishnakumar Sukhramdas
Issue Date
1989
Doctoral Committee Chair(s)
Rendell, Larry A.
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)
Information Science
Computer Science
Language
eng
Abstract
This thesis describes a novel approach to machine learning, based on the principle of learning by reasoning. Current learning systems have significant limitations like brittleness, i.e. the deterioration of performance on a different domain or problem and lack of power required for handling real-world learning problems. The goal of my research was to develop an approach in which many of these limitations are overcome in a unified, coherent and general frame-work. This learning approach is based on techniques of reasoning, such as the discovery of the underlying principle and the recognition of the deeper basis of similarity which emulate human learning. In this thesis, the limitations of current systems are discussed, along with the arguments for using a different approach to learning. This thesis then presents a Reasoning-based Approach to machine learning the author developed, along with the details of a computer implementation. It also illustrates the approach on some learning problems not directly solvable by previous approaches.
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