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/427586/8932047
m code_moc.m
function [codebook,scheme] = code_MOC(m)
% Generate the codebook for multiclass classification with Minimum Output encoding.
%
% >> codebook = code_MOC(m)
%
% see also:
% code
% Copyright (c) 200
www.eeworm.com/read/183445/9158701
m code_moc.m
function [codebook,scheme] = code_MOC(m)
% Generate the codebook for multiclass classification with Minimum Output encoding.
%
% >> codebook = code_MOC(m)
%
% see also:
% code
% Copyright (c) 200
www.eeworm.com/read/376249/9323374
h ctype.h
/*
* Character classification macros for MICRO-C
*
* These macros classify the passed character based on a table
* lookup. The accepted range of character values which may be
* tested is (0
www.eeworm.com/read/374698/9388882
m code_moc.m
function [codebook,scheme] = code_MOC(m)
% Generate the codebook for multiclass classification with Minimum Output encoding.
%
% >> codebook = code_MOC(m)
%
% see also:
% code
% Copyright (c) 200
www.eeworm.com/read/374053/9422746
txt bp文件夹说明.txt
BP神经网络用于分类与回归
----------------------------------------------
----------------------------------------------
文件说明：
1、NeuralNetwork_BP_Classification.m - 分类
2、NeuralNetwork_BP_Regression.m -
www.eeworm.com/read/373627/9446110
index
SOM Self-Organizing Maps: Online Algorithm
batchSOM Self-Organizing Maps: Batch Algorithm
condense Condense training set for k-NN classifier
knn
www.eeworm.com/read/278889/10490576
m code_moc.m
function [codebook,scheme] = code_MOC(m)
% Generate the codebook for multiclass classification with Minimum Output encoding.
%
% >> codebook = code_MOC(m)
%
% see also:
% code
% Copyright (c) 200
www.eeworm.com/read/421949/10676120
m code_moc.m
function [codebook,scheme] = code_MOC(m)
% Generate the codebook for multiclass classification with Minimum Output encoding.
%
% >> codebook = code_MOC(m)
%
% see also:
% code
% Copyright (c) 200
www.eeworm.com/read/299984/7140542
m lssvc.m
function W = lssvc(A, TYPE, PAR, C)
%LSSVC Least-Squares Support Vector Classifier
%
% W = lssvc(A,TYPE,PAR,C);
%
% INPUT
% A dataset
% TYPE Type of the kernel (optional; default: '
www.eeworm.com/read/460435/7251018
m lssvc.m
function W = lssvc(A, TYPE, PAR, C)
%LSSVC Least-Squares Support Vector Classifier
%
% W = lssvc(A,TYPE,PAR,C);
%
% INPUT
% A dataset
% TYPE Type of the kernel (optional; default: '