代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/150760/12265824
c knnclass_mex.c
/*---------------------------------------------------------------------------
knnclass_mex.c: MEX-file code K-NN classifier.
Compile: knnclass_mex.c
www.eeworm.com/read/213492/15133690
c knnclass_mex.c
/*---------------------------------------------------------------------------
knnclass_mex.c: MEX-file code K-NN classifier.
Compile: knnclass_mex.c
www.eeworm.com/read/245316/4504413
java association.java
package javax.jmi.model;
public interface Association extends Classifier {
public boolean isDerived();
public void setDerived(boolean newValue);
}
www.eeworm.com/read/245316/4504451
java mofclass.java
package javax.jmi.model;
public interface MofClass extends Classifier {
public boolean isSingleton();
public void setSingleton(boolean newValue);
}
www.eeworm.com/read/175689/5343520
c knnclass_mex.c
/*---------------------------------------------------------------------------
knnclass_mex.c: MEX-file code K-NN classifier.
Compile: knnclass_mex.c
www.eeworm.com/read/429426/1948850
py randomclassifier.py
# Description: Shows a classifier that makes random decisions
# Category: classification
# Classes: RandomClassifier
# Uses: lenses
# Referenced: RandomClassifier.htm
import oran
www.eeworm.com/read/428780/1954194
c knnclass_mex.c
/*---------------------------------------------------------------------------
knnclass_mex.c: MEX-file code K-NN classifier.
Compile: knnclass_mex.c
www.eeworm.com/read/409299/2234800
svn-base primalnearestneighbours.m.svn-base
function [trainInfo, testInfo, classifierInfo] = primalNearestNeighbours(trainX, trainY, testX, params);
%A classifier based on the nearest neighbour algorithm
%Inputs
%trainX - the input traini
www.eeworm.com/read/411674/11233783
c knnclass_mex.c
/*---------------------------------------------------------------------------
knnclass_mex.c: MEX-file code K-NN classifier.
Compile: knnclass_mex.c
www.eeworm.com/read/431675/8661684
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