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/13911/286798
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/13911/286826
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/13911/286982
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/13911/287004
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/13911/287032
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/482538/1287675
hh checklength.hh
#ifndef CLICK_CHECKLENGTH_HH
#define CLICK_CHECKLENGTH_HH
#include
CLICK_DECLS
/*
=c
CheckLength(LENGTH)
=s classification
drops large packets
=d
CheckLength checks every pack
www.eeworm.com/read/482538/1287753
hh strideswitch.hh
#ifndef CLICK_STRIDESWITCH_HH
#define CLICK_STRIDESWITCH_HH
#include "elements/standard/stridesched.hh"
CLICK_DECLS
/*
* =c
* StrideSwitch(TICKETS0, ..., TICKETSI)
* =s classification
* send
www.eeworm.com/read/251838/4414523
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
www.eeworm.com/read/251522/4418873
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
www.eeworm.com/read/215485/4903485
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