代码搜索：classification

找到约 3,679 项符合「classification」的源代码

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m svc.m

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
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www.eeworm.com/read/438780/7727099

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
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www.eeworm.com/read/298871/7928748

m nnd10lc.m

function nnd10lc(cmd,arg1,arg2,arg3) %NND10LC Linear pattern classification demonstration. % First Version, 8-31-95. %================================================================== % CON
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www.eeworm.com/read/143745/12847743

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
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www.eeworm.com/read/143441/12874910

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
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www.eeworm.com/read/329420/12955646

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
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www.eeworm.com/read/324304/13273554

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
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www.eeworm.com/read/319942/13439040

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
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www.eeworm.com/read/301504/13858039

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
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www.eeworm.com/read/140847/5779146

m hme_class_plot.m

function fh=hme_class_plot(net, nodes_info, train_data, test_data) % % Use this function ONLY when the input dimension is 2 % and the problem is a classification one. % We assume that each row of
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