代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/131588/14136134
m ls.m
function [D, w] = LS(train_features, train_targets, weights, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% Weights - Wei
www.eeworm.com/read/131588/14136166
m discrete_bayes.m
function D = Discrete_Bayes(train_features, train_targets, cost, region, test_feature)
% Classify discrete features using the Bayes decision theory
% Inputs:
% features - Train features
% targ
www.eeworm.com/read/131588/14136405
m rbf_network.m
function [D, mu, Wo] = RBF_Network(train_features, train_targets, Nh, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train features
% t
www.eeworm.com/read/129915/14217584
m ls.m
function [D, w] = LS(train_features, train_targets, weights, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% Weights - Wei
www.eeworm.com/read/129915/14217610
m discrete_bayes.m
function D = Discrete_Bayes(train_features, train_targets, cost, region, test_feature)
% Classify discrete features using the Bayes decision theory
% Inputs:
% features - Train features
% targ
www.eeworm.com/read/129915/14217782
m rbf_network.m
function [D, mu, Wo] = RBF_Network(train_features, train_targets, Nh, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train features
% t
www.eeworm.com/read/216502/4890516
1 file.1
.TH FILE 1
.SH NAME
file \- determine file type
.SH SYNOPSIS
.B file
file ...
.SH DESCRIPTION
.I File
performs a series of tests on each argument
in an attempt to classify it.
If an argument appears
www.eeworm.com/read/209211/4984252
c ti.c
/* ti.c: classify line intersections */
# include "t.h"
/* determine local environment for intersections */
int
interv(int i, int c)
{
int ku, kl;
if (c >= ncol || c == 0) {
if (dboxflg) {
if
www.eeworm.com/read/414590/2144614
h node.h
/* Node.H
*
* The decision tree is built from nodes.
*
* To classify an example, we start at the node of the decision tree
* and work our way down until we reach a yes or no answer. Each
* node
www.eeworm.com/read/414590/2144617
c node.c
/* Node.C
*
* The decision tree is built from nodes.
*
* To classify an example, we start at the node of the decision tree
* and work our way down until we reach a yes or no answer. Each
* node