代码搜索：kernel
找到约 10,000 项符合「kernel」的源代码
代码结果 10,000
www.eeworm.com/read/389322/8533439
m main_svm_one_class.m
% 支持向量机Matlab工具箱1.0 - One-Class SVM, 一类支持向量机
% 使用平台 - Matlab6.5
% 版权所有：陆振波，海军工程大学
% 电子邮件：luzhenbo@yahoo.com.cn
% 个人主页：http://luzhenbo.88uu.com.cn
% 参数文献：Chih-Chung Chang, Chih-Jen Lin. "LIBSVM: a
www.eeworm.com/read/389322/8533442
m main_svc_nu.m
% 支持向量机Matlab工具箱1.0 - Nu-SVC, Nu二类分类算法
% 使用平台 - Matlab6.5
% 版权所有：陆振波，海军工程大学
% 电子邮件：luzhenbo@yahoo.com.cn
% 个人主页：http://luzhenbo.88uu.com.cn
% 参数文献：Chih-Chung Chang, Chih-Jen Lin. "LIBSVM: a Libr
www.eeworm.com/read/289710/8533812
m nobias.m
function nb = nobias(ker)
%NOBIAS returns true if SVM kernel has no implicit bias
%
% Usage: nb = nobias(ker)
%
% Parameters: ker - kernel type
%
% Author: Steve Gunn (srg@ecs.soton.a
www.eeworm.com/read/289680/8535024
m rbf.m
function ker = rbf(arg)
% RBF
%
% Construct a Gaussian radial basis function (RBF) kernel object,
%
% K(x1, x2) = exp(-gamma.*sum((x1 - x2).^2))
%
% Examples:
%
% % default constructo
www.eeworm.com/read/289680/8535031
m evaluate.m
function K = evaluate(ker, x1, x2)
% EVALUATE
%
% Evaluate a Gaussian radial basis kernel, for example
%
% K = evaluate(kernel, x1, x2);
%
% where x1 and x2 are matrices containing input p
www.eeworm.com/read/289680/8535038
c evaluate.c
/******************************************************************************
File : @rbf/evaluate.c
Date : Saturday 18th March 2000
Author : Dr Gavin C. Cawley
Descr
www.eeworm.com/read/289680/8535097
cpp smosvctrain.cpp
/******************************************************************************
File : smotrain.c
Date : Wednesday 13th September 2000
Author : Dr Gavin C. Cawley
Descri
www.eeworm.com/read/289680/8535133
m polynomial.m
function ker = polynomial(arg)
% POLYNOMIAL
%
% Construct a polynomial kernel object,
%
% K(x1, x2) = (x1*x2' + 1).^d;
%
% Examples:
%
% % default constructor (quadratic kernel, d = 2
www.eeworm.com/read/289680/8535136
m evaluate.m
function K = evaluate(ker, x1, x2)
% EVALUATE
%
% Evaluate a polynomial kernel, for example
%
% K = evaluate(kernel, x1, x2);
%
% where x1 and x2 are matrices containing input patterns, wh
www.eeworm.com/read/389274/8536664
m svc.m
function net = svc(arg, sv, w, bias)
%
if nargin == 0
% this is the default constructor
net.kernel = linear;
net.sv = [];
net.w = [];
net.bias = 0;