代码搜索：Classifier
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www.eeworm.com/read/357125/10215870
java multiknn.java
package mulan.classifier;
import weka.core.EuclideanDistance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;
import weka.core.neighboursearch.LinearNNSearch;
www.eeworm.com/read/357125/10215871
java multilabelknn.java
package mulan.classifier;
import java.util.Random;
import weka.core.EuclideanDistance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.neighboursearch.LinearNNSearch;
www.eeworm.com/read/159921/10588306
m oaoclass.m
function [labels,votes] = oaoclass(data,model)
% OAOCLASS One-Against-One SVM classifier.
% [labels,votes] = oaoclass(data,model)
%
% Inputs:
% data [dim x num_data] data to be classified.
% Model [
www.eeworm.com/read/421949/10676980
m oaoclass.m
function [labels,votes] = oaoclass(data,model)
% OAOCLASS One-Against-One SVM classifier.
% [labels,votes] = oaoclass(data,model)
%
% Inputs:
% data [dim x num_data] data to be classified.
% Model [
www.eeworm.com/read/418695/10935183
m minc.m
%MINC Minimum combining classifier
%
% W = minc(V)
% W = V*minc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the minimum combiner: it selects the cla
www.eeworm.com/read/418695/10935438
m meanc.m
%MEANC Averaging combining classifier
%
% W = meanc(V)
% W = V*meanc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the mean combiner: it selects the c
www.eeworm.com/read/418695/10935484
m majorc.m
%MAJORC Majority combining classifier
%
% W = majorc(V)
% W = v*majorc
%
% If V = [V1,V2,V3,...] is a stacked set of classifiers trained for
% the same classes and W is the majority combiner: it se
www.eeworm.com/read/469416/6976425
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% pe
www.eeworm.com/read/265028/6997207
m svmtrain.m
function net = svmtrain(net, X, Y, alpha0, dodisplay)
% SVMTRAIN - Train a Support Vector Machine classifier
%
% NET = SVMTRAIN(NET, X, Y)
% Train the SVM given by NET using the training data X w
www.eeworm.com/read/299984/7140012
m bpxnc.m
%BPXNC Back-propagation trained feed-forward neural net classifier
%
% [W,HIST] = BPXNC (A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each h