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/417431/10990100

m svc.m

function [nsv, alpha, b0] = svc(X,Y,ker,C) %SVC Support Vector Classification % % Usage: [nsv alpha bias] = svc(X,Y,ker,C) % % Parameters: X - Training inputs % Y - Training t
www.eeworm.com/read/417218/10999795

m svc.m

function [nsv, alpha, b0] = svc(X,Y,ker,C) %SVC Support Vector Classification % % Usage: [nsv alpha bias] = svc(X,Y,ker,C) % % Parameters: X - Training inputs % Y - Training t
www.eeworm.com/read/416589/11020310

m svc.m

function [nsv, alpha, b0] = svc(X,Y,ker,C) %SVC Support Vector Classification % % Usage: [nsv alpha bias] = svc(X,Y,ker,C) % % Parameters: X - Training inputs % Y - Training t
www.eeworm.com/read/470924/6901768

m svc.m

function [nsv, alpha, b0] = svc(X,Y,ker,C) %SVC Support Vector Classification % % Usage: [nsv alpha bias] = svc(X,Y,ker,C) % % Parameters: X - Training inputs % Y - Training t
www.eeworm.com/read/469123/6977875

m demo_laplace_usps.m

% Demo script to illustrate use of binaryLaplaceGP.m on a binary digit % classification task. 2006-03-29. if isempty(regexp(path,['gpml' pathsep])) cd ..; w = pwd; addpath([w, '/gpml']); cd gpml-de
www.eeworm.com/read/468922/6981942

m knn.m

function [p,item,c]=knn(trx,tstx,lblx,k) % This code use to compute the Knn classification % This code is edited by Eng. Alaa Tharwat Abd El. Monaaim Othman from Egypt % Teaching assistant in El S
www.eeworm.com/read/462857/7194006

htm dpue080.htm

Delphi Projekt / 躡ung: Einf黨rung zu zyklischen Strukturen
www.eeworm.com/read/462857/7194071

htm delphi01.htm

Einf黨rung in Delphi
www.eeworm.com/read/455967/7360560

asv svc.asv

function [nsv, alpha, b0,t] = svc(X,Y,ker,C) %SVC Support Vector Classification % % Usage: [nsv alpha bias] = svc(X,Y,ker,C) % % Parameters: X - Training inputs % Y - Training
www.eeworm.com/read/455967/7360584

m oldsvc.m

function [nsv, alpha, b0,t] = oldsvc(X,Y,ker,C) %SVC Support Vector Classification % % Usage: [nsv alpha bias] = svc(X,Y,ker,C) % % Parameters: X - Training inputs % Y - Train