代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/384944/8827751
m rocdemo.m
%
% ROCDEMO - demonstrate use of ROC tools
%
% An ROC (receiver operator characteristic) curve is a plot of the true
% positive rate as a function of the false positive rate of a classifier
%
www.eeworm.com/read/357125/10215867
java abstractmultilabelclassifier.java
package mulan.classifier;
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free
www.eeworm.com/read/357125/10215869
java rakel.java
package mulan.classifier;
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free
www.eeworm.com/read/418695/10935162
m cnormc.m
%CNORMC Classifier normalisation for good posteriori probabilities
%
% W = cnormc(W,A)
%
% The mapping W is scaled according to the dataset A in such a
% way that A*W*classc represents as good as
www.eeworm.com/read/397106/8067803
m pnn_vc.m
% Learns classifier and classifies test set
% using a Probabilistic Neural Network
% Usage
% [trainError, testError, estTrainLabels, estTestLabels] = ...
% PNN_VC(trainFeatures, trainLa
www.eeworm.com/read/397102/8067976
m cnormc.m
%CNORMC Classifier normalisation for good posteriori probabilities
%
% W = cnormc(W,A)
%
% The mapping W is scaled according to the dataset A in such a
% way that A*W*classc represents as good as
www.eeworm.com/read/342008/12046768
m cnormc.m
%CNORMC Classifier normalisation for good posteriori probabilities
%
% W = cnormc(W,A)
%
% The mapping W is scaled according to the dataset A in such a
% way that A*W*classc represents as good as
www.eeworm.com/read/429426/1948675
py fss7.py
# Author: B Zupan
# Version: 1.0
# Description: Shows the use of feature subset selection and compares
# plain naive Bayes (with discretization) and the same classifier but wi
www.eeworm.com/read/429426/1948886
py cb-splitconstructor.py
# Description: Shows how to derive a Python class from orange.TreeSplitConstructor
# Category: classification, decision trees, callbacks to Python
# Classes: TreeSplitConstructor, Classifier,
www.eeworm.com/read/293183/8310149
m cnormc.m
%CNORMC Classifier normalisation for good posteriori probabilities
%
% W = cnormc(W,A)
%
% The mapping W is scaled according to the dataset A in such a
% way that A*W*classc represents as good as