代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/425546/10349190
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. Th
www.eeworm.com/read/353969/10401030
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
www.eeworm.com/read/424119/10490976
c perf_classif.c
/*
perf_classif : Returns the Classification Rate and the confusion matrix.
Usage
-------
[R , mat_conf ] = perf_classif(ytest , ytest_est , [m]);
Inputs
-------
ytest
www.eeworm.com/read/349842/10796910
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
www.eeworm.com/read/186874/6971085
txt readme.txt
The code contained in this package is described in
Mean shift based clustering in high dimensions:
A texture classification example.
B. Georgescu, I. Shimshoni, P. Meer
in the proceedings of
www.eeworm.com/read/469416/6976477
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. Th
www.eeworm.com/read/469416/6976511
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. Th
www.eeworm.com/read/299984/7140530
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
www.eeworm.com/read/460435/7251005
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