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wip/pcp,
Machine learning program for pattern classification
Branch: CURRENT,
Version: 2.2,
Package name: pcp2.2,
Maintainer: pkgsrcusersPCP (Pattern Classification Program) is an opensource
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)
* leastsquares (pseudoinverse) linear discriminant
* kNearest Neighbor (kNN)
* neural networks (MultiLayer Perceptron (MLP))
* Support Vector Machine (SVM) algorithm
* SVM, MLP and kNN model selection
* crossvalidation
* bagging (committee) classification
Required to run:[
lang/gcc7]
Required to build:[
pkgtools/cwrappers]
Master sites:
RMD160: c3606f5af124ed604d9d69eaa85719bb1a9f0d8e
Filesize: 2646.717 KB
Version history: (Expand)
 (20200929) Package has been reborn
 (20200929) Package deleted from pkgsrc
 (20200102) Package has been reborn
 (20191217) Package deleted from pkgsrc
 (20191215) Package has been reborn
 (20191214) Package deleted from pkgsrc
CVS history: (Expand)
20121112 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.

20121005 10:46:08 by Aleksej Saushev  Files touched by this commit (13) 
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "userdestdir" is default these days.
Mark packages that don't or might probably not have staged installation.

20120910 02:11:39 by Kamel Derouiche  Files touched by this commit (1) 
Log message:
Update MAINTAINER

20100902 13:56:17 by Kamel Derouiche  Files touched by this commit (4)  
Log message:
Import pcp2.2 as wip/pcp.
PCP (Pattern Classification Program) is an opensource
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)
* leastsquares (pseudoinverse) linear discriminant
* kNearest Neighbor (kNN)
* neural networks (MultiLayer Perceptron (MLP))
* Support Vector Machine (SVM) algorithm
* SVM, MLP and kNN model selection
* crossvalidation
* bagging (committee) classification
