代码搜索：Regularization
找到约 355 项符合「Regularization」的源代码
代码结果 355
www.eeworm.com/read/257015/4366759
m test6.m
% DEMONSTRATION PROGRAM FOR ILLUSTRATING THE EFFECT OF REGULARIZATION
%
% Programmed by Magnus Norgaard, IAU/IMM/EI, Technical Univ. of Denmark
% LastEditDate: Aug 21, 1995.
close all
StopDemo=
www.eeworm.com/read/415313/11076730
m ldakernel_classify.m
% LDAKernel_classify: implementation for kernel linear discriminant analysis
%
% Parameters:
% para: parameters
% 1. RegFactor: regularization factor, default: 0.1
% 2. Kernel: kernel type,
www.eeworm.com/read/245836/12778254
todo
BUGS: no known bugs
SOLVER:
+ bc_abso: handle "side" internally
+ more friction laws: velocity-weakening, rate-and-state, bimaterial regularization
+ Rayleigh damping
+ atten
www.eeworm.com/read/415313/11076778
m logitregkernel.m
% LogitRegKernel: implementation for kernel logistic regression
%
% Parameters:
% para: parameters
% 1. RegFactor: regularization factor, default: 0
% 2. Kernel: kernel type, 0: linear, 1: p
www.eeworm.com/read/415313/11076784
m lda_classify.m
% LDA_classify: implementation for linear discriminant analysis
%
% Parameters:
% para: parameters
% 1. RegFactor: regularization factor, default: 0
% 2. QDA: use qudratic discriminant analy
www.eeworm.com/read/200886/15420921
m getsmoothlikeind.m
% function likTerm = getSmoothLikeInd(G,z,u)
%
% calculate the regularization part of the log likelihood
% -lambda*sum(diff(z).^2)
%
% returns one component per class
function likTerm = getSmooth
www.eeworm.com/read/112466/15484747
tex model.tex
\rhead{class MODEL}
\section{MODEL : Shape Learning Class}
{\tt MODEL} provides shape matrix and local regularization parameters learning routines. It has the following structure:
\begin{verbat
www.eeworm.com/read/386050/8768269
m mogc.m
%MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
%
% INPUT
% A Dataset
% N Number of mixtures (optional; default 2)
% R,S Regularization parameters, 0
www.eeworm.com/read/170249/9813437
m test6.m
% DEMONSTRATION PROGRAM FOR ILLUSTRATING THE EFFECT OF REGULARIZATION
%
% Written by Magnus Norgaard, IAU/IMM, Technical Univ. of Denmark
% LastEditDate: Jan. 15, 2000.
close all
StopDemo=0;
f
www.eeworm.com/read/299984/7140350
m mogc.m
%MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
%
% INPUT
% A Dataset
% N Number of mixtures (optional; default 2)
% R,S Regularization parameters, 0