代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/289334/8558633
m knnrule.m
function model=knnrule(data,K)
% KNNRULE Creates K-nearest neighbours classifier.
%
% Synopsis:
% model=knnrule(data)
% model=knnrule(data,K)
%
% Description:
% It creates model of the K-nearest ne
www.eeworm.com/read/286180/8784149
m contents.m
% OSU Support Vector Machines (SVMs) Toolbox
% version 3.00, Feb. 2002
%
% The core of this toolbox is based on Dr. Lin's Lib SVM version 2.33
% For more details, please see:
% http://www.csie.n
www.eeworm.com/read/428849/8834640
m knnrule.m
function model=knnrule(data,K)
% KNNRULE Creates K-nearest neighbours classifier.
%
% Synopsis:
% model=knnrule(data)
% model=knnrule(data,K)
%
% Description:
% It creates model of the K-nearest ne
www.eeworm.com/read/428849/8834904
m fldqp.m
function model = fldqp(data)
% FLDQP Fisher Linear Discriminat using Quadratic Programming.
%
% Synopsis:
% model = fldqp( data )
%
% Description:
% This function computes the binary linear classifi
www.eeworm.com/read/283653/9000597
m contents.m
% OSU Support Vector Machines (SVMs) Toolbox
% version 3.00, Feb. 2002
%
% The core of this toolbox is based on Dr. Lin's Lib SVM version 2.33
% For more details, please see:
% http://www.csie.n
www.eeworm.com/read/181390/9256385
m contents.m
% OSU Support Vector Machines (SVMs) Toolbox
% version 3.00, Feb. 2002
%
% The core of this toolbox is based on Dr. Lin's Lib SVM version 2.33
% For more details, please see:
% http://www.csie.n
www.eeworm.com/read/180304/9313072
m contents.m
% OSU Support Vector Machines (SVMs) Toolbox
% version 3.00, Feb. 2002
%
% The core of this toolbox is based on Dr. Lin's Lib SVM version 2.33
% For more details, please see:
% http://www.csie.n
www.eeworm.com/read/175317/9552393
m ml_train.m
function classifier=ml_train(X,Y,options, method)
% ML_TRAIN Trains a classifier with some "options" using some "method"
% ----------------------------------------------------------------------------
www.eeworm.com/read/362246/10010115
m knnrule.m
function model=knnrule(data,K)
% KNNRULE Creates K-nearest neighbours classifier.
%
% Synopsis:
% model=knnrule(data)
% model=knnrule(data,K)
%
% Description:
% It creates model of the K-nearest ne
www.eeworm.com/read/362246/10010434
m fldqp.m
function model = fldqp(data)
% FLDQP Fisher Linear Discriminat using Quadratic Programming.
%
% Synopsis:
% model = fldqp( data )
%
% Description:
% This function computes the binary linear classifi