代码搜索：classification
找到约 3,679 项符合「classification」的源代码
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www.eeworm.com/read/408460/11387512
h ctype.h
/***
*ctype.h - character conversion macros and ctype macros
*
* Copyright (c) 1985-1990, Microsoft Corporation. All rights reserved.
*
*Purpose:
* Defines macros for character classification/c
www.eeworm.com/read/401180/11563148
m svm_classify.m
function status = svm_classify(options, data, model, predictions)
% SVM_CLASSIFY - Interface to SVM light, classification module
%
% STATUS = SVM_CLASSIFY(OPTIONS, DATA, MODEL, PREDICTIONS)
% C
www.eeworm.com/read/400577/11572599
m gendats.m
%GENDATS Generation of a simple classification problem of 2 Gaussian classes
%
% A = GENDATS (N,K,D,LABTYPE)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]
www.eeworm.com/read/400577/11572999
m gentrunk.m
%GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K
www.eeworm.com/read/400577/11573015
m featself.m
%FEATSELF Forward feature selection for classification
%
% [W,R] = FEATSELF(A,CRIT,K,T,FID)
% [W,R] = FEATSELF(A,CRIT,K,N,FID)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or u
www.eeworm.com/read/400577/11573217
m fdsc.m
%FDSC Feature based Dissimilarity Space Classification (outdated)
%
% This routine is outdated, use KERNELC instead
%
% W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF)
% W = A*FDSC([],R,FEATMAP,TYPE,P,C
www.eeworm.com/read/400577/11573361
m featselb.m
%FEATSELB Backward feature selection for classification
%
% [W,R] = FEATSELB(A,CRIT,K,T,FID)
% [W,R] = FEATSELB(A,CRIT,K,N,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion o
www.eeworm.com/read/400577/11573364
m featsellr.m
%FEATSELLR Plus-L-takeaway-R feature selection for classification
%
% [W,RES] = FEATSELLR(A,CRIT,K,L,R,T,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
www.eeworm.com/read/260625/11716692
m demsvm2.m
function demsvm2()
% DEMSVM2 - Demonstrate advanced Support Vector Machine features
%
% DEMSVM2 demonstrates the classification of a simple artificial data
% set by a Support Vector Machine class
www.eeworm.com/read/255755/12057237
m gendats.m
%GENDATS Generation of a simple classification problem of 2 Gaussian classes
%
% A = GENDATS (N,K,D,LABTYPE)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]