代码搜索：classification

找到约 3,679 项符合「classification」的源代码

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m demglm1.m

%DEMGLM1 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/339665/12211944

m demglm2.m

%DEMGLM2 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/150905/12249295

m costm.m

%COSTM Cost mapping, classification using costs % % Y = COSTM(X,C,LABLIST) % W = COSTM([],C,LABLIST) % % DESCRIPTION % Maps the classifier output X (assumed to be posterior probability % estimate
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www.eeworm.com/read/150905/12250588

m demglm1.m

%DEMGLM1 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/150905/12250686

m demglm2.m

%DEMGLM2 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/149739/12353575

m costm.m

%COSTM Cost mapping, classification using costs % % Y = COSTM(X,C,LABLIST) % W = COSTM([],C,LABLIST) % % DESCRIPTION % Maps the classifier output X (assumed to be posterior probability % estimate
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www.eeworm.com/read/249982/12443622

m evaluate_tree_performance.m

function [score,outputs] = evaluate(CPD, fam, data, ns, cnodes) % Evaluate evaluate the performance of the classification/regression tree on given complete data % score = evaluate(CPD, fam, data, ns
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www.eeworm.com/read/132026/14113423

txt 数据挖掘中cart算法实现.txt

CART function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targe
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www.eeworm.com/read/131588/14136382

m cart.m

function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targets % para
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www.eeworm.com/read/130490/14190284

c multi_ecoc.c

/* Copyright (C) 2001-2002 Mikael Ylikoski * See the accompanying file "README" for the full copyright notice */ /** * @file * Multi class classification using error correcting output codes. * *
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