代码搜索：Classifier
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www.eeworm.com/read/386050/8767500
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
www.eeworm.com/read/386050/8768184
m roc.m
%ROC Receiver-Operator Curve
%
% E = ROC(A,W,C,N)
% E = ROC(B,C,N)
%
% INPUT
% A Dataset
% W Trained classifier, or
% B Classification result, B = A*W*CLASSC
% C Index of desired clas
www.eeworm.com/read/384512/8866328
m knn.m
function [C,P]=knn(d, Cp, K)
%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
%
% [C,P]=knn(d, Cp, [K])
%
% Input and output arguments ([]'s are optional):
% d (matrix)
www.eeworm.com/read/180305/9313027
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 wi
www.eeworm.com/read/376053/9334410
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/178917/9382329
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 wi
www.eeworm.com/read/175683/9536326
asv svmtrain.asv
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 wi
www.eeworm.com/read/175683/9536378
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 wi
www.eeworm.com/read/365739/9849767
m hamming_loss.m
function HammingLoss=Hamming_loss(Pre_Labels,test_target)
%Computing the hamming loss
%Pre_Labels: the predicted labels of the classifier, if the ith instance belong to the jth class, Pre_Labels(j,i
www.eeworm.com/read/357125/10215862
java subsetmapper.java
package mulan.classifier;
import mulan.Statistics;
import mulan.*;
import weka.core.*;
import java.io.Serializable;
import java.util.*;
/*
* Maps a predicted set of labels to the nearest s