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/130490/14190029
h holders.h
#ifndef HOLDERS_H
#define HOLDERS_H
#include "doc_classifier.h"
#include "multi.h"
#include "stemmer.h"
int
holders_load (const char *dir);
void
holders_free (void);
multi_functions *
holders_find
www.eeworm.com/read/175317/9552350
m experiment_moon.m
function [X,Y1]=experiment_moon(X,Y,XT,YT,method,q,s);
% 2 Moons Experiment
% Author: Vikas Sindhwani (vikass@cs.uchicago.edu)
options=ml_options('gamma_A',0.1, 'NN',6, 'Kernel','rbf','KernelParam',
www.eeworm.com/read/190459/8443043
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/431675/8662067
m binm.m
%BINM Binary mapping for classifier outcomes
%
% W = W*binm
%
% Binary transformation of a map or a classifier.
%
% binm transforms the outcomes of the classifier or map
% to binary using the maxim
www.eeworm.com/read/386050/8767372
m minc.m
%MINC Minimum combining classifier
%
% W = MINC(V)
% W = V*MINC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Minimum combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ...
www.eeworm.com/read/386050/8767591
m parzenc.m
%PARZENC Optimisation of the Parzen classifier
%
% [W,H] = PARZENC(A)
% W = PARZENC(A,H,FID)
%
% INPUT
% A dataset
% H smoothing parameter (may be scalar, vector of per-class
% param
www.eeworm.com/read/386050/8768061
m adaboostc.m
%ADABOOSTC
%
% [W,V,ALF] = ADABOOSTC(A,CLASSF,N,RULE,VERBOSE);
%
% INPUT
% A Dataset
% CLASSF Untrained weak classifier
% N Number of classifiers to be trained
% RULE Combinin
www.eeworm.com/read/386050/8768204
m votec.m
%VOTEC Voting combining classifier
%
% W = VOTEC(V)
% W = V*VOTEC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Voting combiner
%
% DESCRIPTION
% If V = [V1,V2,V3,...] is a stacked set of
www.eeworm.com/read/386050/8769549
m maxc.m
%MAXC Maximum combining classifier
%
% W = MAXC(V)
% W = V*MAXC
%
% INPUT
% V Stacked set of classifiers
%
% OUTPUT
% W Combined classifier using max-rule
%
% DESCRIPTION
% If V = [V1,V2,V
www.eeworm.com/read/429504/8804765
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)