CMU Artificial Intelligence Repository
Home INFO Search FAQs Repository Root

PEBLS: Parallel Exemplar-Based Learning System

PEBLS (Parallel Exemplar-Based Learning System) is a nearest-neighbor learning system designed for applications where the instances have symbolic feature values. PEBLS has been applied to the prediction of protein secondary structure and to the identification of DNA promoter sequences. PEBLS incorporates a number of features intended to support flexible experimentation in symbolic domains. We have provided support for k-nearest neighbor learning, and the ability to choose among different techniques for weighting both exemplars and individual features. A number of post-processing techniques specific to the domain of protein secondary structure have also been provided.
Origin:    []

Version: 3.0 (4-OCT-94) Requires: ANSI C Copying: Copyright (c) 1993 by The Johns Hopkins University Use, copying, modification and distribution permitted for research purposes only. Any commercial or for-profit use of PEBLS 3.0 is strictly prohibited without the express written consent of Prof. Steven Salzberg, Department of Computer Science, The Johns Hopkins University. Updated: Fri Oct 7 15:20:35 1994 CD-ROM: Prime Time Freeware for AI, Issue 1-1 Author(s): Steven Salzberg Dept. of Computer Science Johns Hopkins University Baltimore, MD 21218 Tel: 410-516-8438 Keywords: Authors!Salzberg, C!Code, Machine Learning!Nearest-Neighbor Learning, PEBLS References: A technical description appears in the article by Cost and Salzberg, Machine Learning journal 10:1 (1993). S. Cost and S. Salzberg. A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features, Machine Learning, 10:1, 57-78 (1993). J. Rachlin, S. Kasif, S. Salzberg, and D. Aha. Towards a Better Understanding of Memory-Based and Bayesian Classifiers. {\it Proceedings of the Eleventh International Conference on Machine Learning} (pp. 242--250). New Brunswick, NJ, July 1994, Morgan Kaufmann Publishers.
Last Web update on Mon Feb 13 10:24:34 1995