Neural plasma

Daniel Berrar, Werner Dubitzky

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

This paper presents a novel type of artificial neural network, called neural plasma, which is tailored for classification tasks involving few observations with a large number of variables. Neural plasma learns to adapt its classification confidence by generating artificial training data as a function of its confidence in previous decisions. In contrast to multilayer perceptrons and similar techniques, which are inspired by topological and operational aspects of biological neural networks, neural plasma is motivated by aspects of high-level behavior and reasoning in the presence of uncertainty. The basic principles of the proposed model apply to other supervised learning algorithms that provide explicit classification confidence values. The empirical evaluation of this new technique is based on benchmarking experiments involving data sets from biotechnology that are characterized by the small-n-large-p problem. The presented study exposes a comprehensive methodology and is seen as a first step in exploring different aspects of this methodology.

Original languageEnglish
Title of host publicationArtificial Intelligence in Theory and Practice
Subtitle of host publicationIFIP 19th World Computer Congress, TC 12: IFIP AI 2006 Stream, August 21-24, 2006, Santiago, Chile
PublisherSpringer New York
Pages159-168
Number of pages10
ISBN (Print)0387346554, 9780387346557
DOIs
Publication statusPublished - 2006 Jan 1

Publication series

NameIFIP International Federation for Information Processing
Volume217
ISSN (Print)1571-5736

ASJC Scopus subject areas

  • Information Systems and Management

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  • Cite this

    Berrar, D., & Dubitzky, W. (2006). Neural plasma. In Artificial Intelligence in Theory and Practice: IFIP 19th World Computer Congress, TC 12: IFIP AI 2006 Stream, August 21-24, 2006, Santiago, Chile (pp. 159-168). (IFIP International Federation for Information Processing; Vol. 217). Springer New York. https://doi.org/10.1007/978-0-387-34747-9_17