---------------------------------------------------------- ANNOUNCEMENT PEBLS 2.0 is now available via Anonymous FTP. ---------------------------------------------------------- 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 based on the primary amino acid sequence of protein sub-units, and to the identification of DNA promoter sequences. A technical description appears in the article by Cost and Salzberg, Machine Learning journal 10:1 (1993). PEBLS 2.0 is a serial version written entirely in ANSI C. PEBLS 2.0 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. TO OBTAIN PEBLS BY ANONYMOUS FTP -------------------------------- The latest version of PEBLS is available free of charge, and may be obtained via anonymous FTP from the Johns Hopkins University Computer Science Department. To obtain a copy of PEBLS, type the following commands: UNIX_prompt> ftp blaze.cs.jhu.edu [Note: the Internet address of blaze.cs.jhu.edu is 128.220.13.50] Name: anonymous Password: [enter your email address] ftp> bin ftp> cd pub/pebls ftp> get pebls.tar.Z ftp> bye [Place the file pebls.tar.Z in a convenient subdirectory.] UNIX_prompt> uncompress pebls.tar.Z UNIX_prompt> tar -xf pebls.tar [Read the files "README" and "pebls.doc"] For further information, contact: Prof. Steven Salzberg Dept. of Computer Science The Johns Hopkins University Baltimore, MD 21210 Email: salzberg@cs.jhu.edu PEBLS 2.0 IS INTENDED FOR RESEARCH PURPOSES ONLY. PEBLS 2.0 may be used, copied, and modified for this purpose. Any commercial use of PEBLS 2.0 is strictly prohibited without the express written consent of Prof. Steven Salzberg, Department of Computer Science, The Johns Hopkins University.