Machine Learning
Aixe PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

This program is a full ANSI C++ compliant version of Jorrit IJpenberg*s Delphi program AIX.For an introduction to that program and related stuff, please consult his web page at: http://tcw2.ppsw.rug.nl/~jorrit/ai/ai.html.The C++ source code is a more or less literary translation of the Object Pascal version,Copyright (C) 1998 by Jorrit IJpenberg.

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Autoclass Dos PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

AutoClass is an unsupervised Bayesian classification system that seeks a maximum posterior probability classification. Key features:

  • determines the number of classes automatically;
  • can use mixed discrete and real valued data;
  • can handle missing values;
  • processing time is roughly linear in the amount of the data;
  • cases have probabilistic class membership;
  • allows correlation between attributes within a class;
  • generates reports describing the classes found; and
  • predicts *test* case class memberships from a *training* classification.

Inputs consist of a database of attribute vectors (cases), either real or discrete valued, and a class model. Default class models are provided. AutoClass finds the set of classes that is maximally probable with respect to the data and model. The output is a set of class descriptions, and partial membership of the cases in the classes. For more details see *Bayesian Classification (AutoClass): Theory and Results* (kdd-95.ps in ~/autoclass-c/doc/), *Bayesian Classification Theory* (tr-fia-90-12-7-01.ps in ~/autoclass-c/doc/). A list of references is included below.

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Autoclass PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

AutoClass is an unsupervised Bayesian classification system that seeks a maximum posterior probability classification. Key features:

  • determines the number of classes automatically;
  • can use mixed discrete and real valued data;
  • can handle missing values;
  • processing time is roughly linear in the amount of the data;
  • cases have probabilistic class membership;
  • allows correlation between attributes within a class;
  • generates reports describing the classes found; and
  • predicts *test* case class memberships from a *training* classification.

Inputs consist of a database of attribute vectors (cases), either real or discrete valued, and a class model. Default class models are provided. AutoClass finds the set of classes that is maximally probable with respect to the data and model. The output is a set of class descriptions, and partial membership of the cases in the classes. For more details see *Bayesian Classification (AutoClass): Theory and Results* (kdd-95.ps in ~/autoclass-c/doc/), *Bayesian Classification Theory* (tr-fia-90-12-7-01.ps in ~/autoclass-c/doc/). A list of references is included below.

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Lispworks PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

Getting Started

Quick TCP/IP Start: Load the file mac-start-server.lisp into MCL 3.0. This will compile the server and launch it running the demo Web structure. This assumes you*re running MacTCP over TCP/IP.

Once you get the Common Lisp Hypermedia Server running, it will serve its own documentation when you access the URL http://your.host.domain/cl-http/cl-http.html

Configuring the server is explained in detail by http://your.host.domain/cl-http/configure.html.

You can use the local file option of your Web browser to read the configuration instructions before starting the server. This file is found in the subdirectory of the cl-http distribution:

/mac-cl-http-n-n/www/cl-http/configure.html

If you have questions, you may direct them to the developer group at You can join this group by sending a message to containing *subscribe www-cl* in the message body.

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