Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms
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Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.
History
The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.
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1969-present
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- College of Liberal Arts and Sciences (parent college)
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Abstract
Constructive learning algorithms offer an approach to incremental construction of near-minimal artificial neural networks for pattern classification. Examples of such algorithms include Tower, Pyramid, Upstart, and Tiling algorithms which construct multilayer networks of threshold logic units (or, multi-layer perceptrons). These algorithms differ in terms of the topology of the networks that they construct which in turn biases the search for a decision boundary that correctly classifies the training set. This paper presents an analysis of such algorithms from a geometrical perspective. This analysis helps in a better characterization of the search bias employed by the different algorithms in relation to the geometrical distribution of examples in the training set. Simple experiments with non linearly separable training sets support the results of mathematical analysis of such algorithms. This suggests the possibility of designing more efficient constructive algorithms that dynamically choose among different biases to build near-minimal networks for pattern classification.