Classification Using Multi-Layered Perceptrons

Abstract

There has been an increasing interest in the applicability of neural networks in disparate domains. In this paper, we describe the use of multi-layered perceptrons, a type of neural network topology, for financial classification problems, with promising results. Back-propagation, which is the learning algorithm most often used in multi-layered perceptrons, however, is inherently an inefficient search procedure. We present improved procedures which have much better convergence properties. Using several financial classification applications as examples, we show the efficacy of using multi-layered perceptrons with improved learning algorithms. The modified learning algorithms have better performance, in terms of classification/prediction accuracies, than the methods previously used in the literature, such as probit analysis and similarity-based learning techniques.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1990
Accession Number
ADA232810

Entities

People

  • James A. Gentry
  • Michael J. Shaw
  • Selwyn Piramuthu

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Classification
  • Computational Processes
  • Computers
  • Computing System Architectures
  • Data Science
  • Data Sets
  • Factor Analysis
  • Information Processing
  • Information Science
  • Machine Learning
  • Neural Networks
  • Regression Analysis
  • Statistical Analysis

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks