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/486079/6543001
m svmtrain.m
function [svm_struct, svIndex] = svmtrain(training, groupnames, varargin)
%SVMTRAIN trains a support vector machine classifier
%
% SVMStruct = SVMTRAIN(TRAINING,GROUP) trains a support vector machin
www.eeworm.com/read/264146/11327612
m average_precision.m
function Average_Precision=Average_precision(Outputs,test_target)
%Computing the average precision
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class
www.eeworm.com/read/264146/11327614
m ranking_loss.m
function RankingLoss=Ranking_loss(Outputs,test_target)
%Computing the hamming loss
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Ou
www.eeworm.com/read/407916/11408595
cpp classmappings.cpp
/*
* This file is part of MultiBoost, a multi-class
* AdaBoost learner/classifier
*
* Copyright (C) 2005-2006 Norman Casagrande
*
* This library is free software; you can redistribute it and/or
* mod
www.eeworm.com/read/407916/11408603
h classmappings.h
/*
* This file is part of MultiBoost, a multi-class
* AdaBoost learner/classifier
*
* Copyright (C) 2005-2006 Norman Casagrande
*
* This library is free software; you can redistribute it and/or
* mod
www.eeworm.com/read/400577/11572980
m nbayesc.m
%NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTP
www.eeworm.com/read/400577/11573184
m neurc.m
%NEURC Automatic neural network classifier
%
% W = NEURC (A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of units
% Default: 0.2 x size smallest class in A.
%
% OUTPUT
% W T
www.eeworm.com/read/400577/11573186
m testp.m
%TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%
www.eeworm.com/read/400577/11573202
m testauc.m
%TESTAUC Multiclass error area under the ROC
%
% E = TESTAUC(A*W)
% E = TESTAUC(A,W)
% E = A*W*TESTAUC
%
% INPUT
% A Dataset to be classified
% W Classifier
%
% OUTPUT
% E Er
www.eeworm.com/read/400577/11573203
m bayesc.m
%BAYESC Bayes classifier
%
% W = BAYESC(WA,WB, ... ,P,LABLIST)
%
% INPUT
% WA, WB, ... Trained mappings for supplying class density estimates
% P Vector with class prior probabili