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/137160/13341899
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/137160/13342265
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/320830/13417575
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/314653/13562257
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/314653/13562518
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/303048/13823280
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/321605/6278455
java bayesiannaive.java
/*
Bao Jie 2002-04-02
Iowa State University
*/
package weka.classifiers;
import java.io.*;
import java.util.*;
import weka.core.*;
public class BayesianNaive extends Classifier {// for
www.eeworm.com/read/402363/6343586
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/493294/6399970
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/493294/6400255
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