代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
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www.eeworm.com/read/129915/14217749
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/38039/1096157
mnu slidervol.mnu
Slider#Volume
#
Ref#Part#Selection
#
Default#Pull#Direction
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Custom#Pull#Direction
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Classify#Bound#Quilts
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Proj#Plane#Selection
#
www.eeworm.com/read/340665/3271096
tex ctype.tex
@node Ctype
@chapter Character Type Macros and Functions (@file{ctype.h})
This chapter groups macros (which are also available as subroutines)
to classify characters into several categories (alphabeti
www.eeworm.com/read/322306/3526372
tex ctype.tex
@node Ctype
@chapter Character Type Macros and Functions (@file{ctype.h})
This chapter groups macros (which are also available as subroutines)
to classify characters into several categories (alphabeti
www.eeworm.com/read/298657/3862036
tex ctype.tex
@node Ctype
@chapter Character Type Macros and Functions (@file{ctype.h})
This chapter groups macros (which are also available as subroutines)
to classify characters into several categories (alphabeti
www.eeworm.com/read/279968/4131246
tex ctype.tex
@node Ctype
@chapter Character Type Macros and Functions (@file{ctype.h})
This chapter groups macros (which are also available as subroutines)
to classify characters into several categories (alphabeti
www.eeworm.com/read/415311/11077177
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/410973/11262412
txt 5-1420msg2.txt
Subject: re : 5 . 1404 comparative method in linguistics
karl teeter be mistake , i think , when he say that you cannot classify language on the basis of phonological correspondence in the lexical it
www.eeworm.com/read/191902/8417051
m parzen.m
function D = parzen(train_features, train_targets, hn, region)
% Classify using the Parzen windows algorithm
% Inputs:
% features - Train features
% targets - Train targets
% hn - No
www.eeworm.com/read/191902/8417053
m ml_diag.m
function D = ML_diag(train_features, train_targets, AlgorithmParameters, region)
% Classify using the maximum likelyhood algorithm with diagonal covariance matrices
% Inputs:
% features - Train