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			ANNOUNCEMENT

	PEBLS 2.0 is now available via Anonymous FTP.
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     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.