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/429878/8784081

htm demev2.htm

Netlab Reference Manual demev2 demev2 Purpose Demonstrate Bayesian classification for the MLP. Synopsis demev2
www.eeworm.com/read/253950/12174098

htm demev2.htm

Netlab Reference Manual demev2 demev2 Purpose Demonstrate Bayesian classification for the MLP. Synopsis demev2
www.eeworm.com/read/150905/12250262

htm demev2.htm

Netlab Reference Manual demev2 demev2 Purpose Demonstrate Bayesian classification for the MLP. Synopsis demev2
www.eeworm.com/read/13911/287354

m cerror.m

function error=cerror(y1,y2,label) % CERROR Computes classification error. % % Synopsis: % error = cerror(y1,y2) % error = cerror(y1,y2,label) % % Description: % error = cerror(y1,y2) returns clas
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asv cerror.asv

function error=cerror(y1,y2,label) % CERROR Computes classification error. % % Synopsis: % error = cerror(y1,y2) % error = cerror(y1,y2,label) % % Description: % error = cerror(y1,y2) returns clas
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asv cerrora.asv

function [text,error]=cerror(y1,y2,label) % CERROR Computes classification error. % % Synopsis: % error = cerror(y1,y2) % error = cerror(y1,y2,label) % % Description: % error = cerror(y1,y2) retur
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m cerrora.m

function [text,error]=cerror(y1,y2,label) % CERROR Computes classification error. % % Synopsis: % error = cerror(y1,y2) % error = cerror(y1,y2,label) % % Description: % error = cerror(y1,y2) retur
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m cerror.m

function error=cerror(y1,y2,label) % CERROR Computes classification error. % % Synopsis: % error = cerror(y1,y2) % error = cerror(y1,y2,label) % % Description: % error = cerror(y1,y2) returns clas
www.eeworm.com/read/175689/5343528

asv cerror.asv

function error=cerror(y1,y2,label) % CERROR Computes classification error. % % Synopsis: % error = cerror(y1,y2) % error = cerror(y1,y2,label) % % Description: % error = cerror(y1,y2) returns clas
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1 ,classify.1

.\" %W% (EPI) %G% .TH CLASSIFY 1\-ESPS %G% .ds ]W "\fI\s+4\ze\h'0.05'e\s-4\v'-0.4m'\fP\(*p\v'0.4m'\ Entropic Speech, Inc. .SH NAME classify \- maximum-likelihood classification of feature records .SH