Biomedical subjects

S G Romaniuk

Publications and source records attributed to S G Romaniuk.

2 recordsLinked to original sources

PubMed1995

Theoretical results for a class of neural networks.

The ability to derive minimal network architectures for neural networks has been at the center of attention for several years now. To this date numerous algorithms have been proposed to automatically construct networks. Unfortunately, these algorithms lack a fundamental theoretical analysis of their capabilities and only empirical evaluations on a few selected benchmark problems exist. Some theoretical results have been provided for small classes of well-known benchmark problems such as parity- and encoder-functions, but these are of little value due to their restrictiveness. In this work we describe a general class of 2-layer networks with 2 hidden units capable of representing a large set of problems. The cardinality of this class grows exponentially with regard to the inputs N. Furthermore, we outline a simple algorithm that allows us to determine, if any function (problem) is a member of this class. The class considered in this paper includes the benchmark problems parity and symmetry. Finally, we expand this class to include an even larger set of functions and point out several interesting properties it exhibits.

Models, Theoretical↗

PubMed1993

Trans-dimensional learning.

The main objective of this research paper is to introduce a novel approach to pattern classification across multiple dimensions. Trans-dimensional learning is concerned with automatically determining network architectures that can generalize not only for fixed-dimensional problems but take a profound step by exemplifying how learning an unrestricted number of problems--which differ in the dimensionality of their input space N--can be accomplished. Relaxing the classification of network units as inputs hidden and outputs leads to the notion of all units of a network being features. Learning is perceived as the process of creating features and integrating these with other constructed features to form a self-adjusting network which can build on prior learned knowledge. This feat is accomplished by utilizing the simple perception rule for local feature training and incorporating evolutionary processes for determining suitable partitions to train individual features on. The basic algorithm introduced here (TDL) is augmented by single feature pruning to further reduce network complexity. An array of experiments is presented to emphasize the learning capabilities of TDL.

Algorithms↗