代码搜索：svc
找到约 5,868 项符合「svc」的源代码
代码结果 5,868
www.eeworm.com/read/351470/10648702
plg svc.plg
礦ision3 Build Log
Project:
E:\XrEmbedded\Z32R\Software\Example\17.1 - SVC\SVC.uv2
Project File Date: 05/06/2008
Output:
www.eeworm.com/read/351470/10648746
c svc.c
__asm void SVC_Handler (void) {
PRESERVE8
TST LR,#4 ; Called from Handler Mode?
MRSNE R12,PSP ; Yes, use PSP
www.eeworm.com/read/421949/10675880
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/418695/10935215
m svc.m
%SVC Support Vector Classifier
%
% [W,J] = svc(A,type,par,C)
%
% Optimizes a support vector classifier for the dataset A by
% quadratic programming. The classifier can be of one of the types
% as
www.eeworm.com/read/418459/10944612
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/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/466896/7022924
m svc.m
%
% SVC: Construct a support vector classifier.
%
% Constructs a support vector classifier based on the data points 'xo'
% (stored rowwise) and labels 'yo', which should be either -1 or 1.
%
% Returns