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/137160/13342337
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/316604/13520499
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/314653/13562555
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/140847/5779336
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
www.eeworm.com/read/133943/5897519
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
www.eeworm.com/read/119864/6081478
c ctype.c
/* ctype.c
* Character classification and conversion
* Copyright (C) 2000 Lineo, Inc.
* Written by Erik Andersen
* This file is part of the uClibc C library and is distributed
* under the GNU Lib
www.eeworm.com/read/264967/6352137
c alloc.c
#include
#include
#include
#include
#include "ga_knn.h"
/*---------------------------------------------------------------
| Sample classification and gen
www.eeworm.com/read/359185/6352566
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/493206/6398576
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/493294/6400309
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