./wip/pcp, Machine learning program for pattern classification

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Branch: CURRENT, Version: 2.2, Package name: pcp-2.2, Maintainer: pkgsrc-users

PCP (Pattern Classification Program) is an open-source
machine learning program for supervised classification
of patterns (vectors of measurements).

PCP implements the following algorithms and methods:

* Fisher's linear discriminant
* dimensionality reduction using Singular Value Decomposition
* Principal Component Analysis
* feature subset selection
* Bayes error estimation
* parametric classifiers (linear and quadratic)
* least-squares (pseudo-inverse) linear discriminant
* k-Nearest Neighbor (k-NN)
* neural networks (Multi-Layer Perceptron (MLP))
* Support Vector Machine (SVM) algorithm
* SVM, MLP and k-NN model selection
* cross-validation
* bagging (committee) classification


Required to run:
[lang/g95]

Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: 1ac8e0d795338645e55321c345eca53382870f08
RMD160: c3606f5af124ed604d9d69eaa85719bb1a9f0d8e
Filesize: 2646.717 KB

Version history: (Expand)


CVS history: (Expand)


   2012-11-12 17:26:41 by othyro | Files touched by this commit (56)
Log message:
MASTER_SITES -> MASTER_SITE_SOURCEFORGE; part 2/4. Let me know if this
breaks anything. Minor formatting and HOMEPAGE fixes in some files.
   2012-10-05 10:46:08 by Aleksej Saushev | Files touched by this commit (13)
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't or might probably not have staged installation.
   2012-09-10 02:11:39 by Kamel Derouiche | Files touched by this commit (1) | Package updated
Log message:
Update MAINTAINER

   2010-09-02 13:56:17 by Kamel Derouiche | Files touched by this commit (4) | Imported package
Log message:
Import pcp-2.2 as wip/pcp.

PCP (Pattern Classification Program) is an open-source
machine learning program for supervised classification
of patterns (vectors of measurements).

PCP implements the following algorithms and methods:

* Fisher's linear discriminant
* dimensionality reduction using Singular Value Decomposition
* Principal Component Analysis
* feature subset selection
* Bayes error estimation
* parametric classifiers (linear and quadratic)
* least-squares (pseudo-inverse) linear discriminant
* k-Nearest Neighbor (k-NN)
* neural networks (Multi-Layer Perceptron (MLP))
* Support Vector Machine (SVM) algorithm
* SVM, MLP and k-NN model selection
* cross-validation
* bagging (committee) classification