代码搜索：Variance
找到约 2,271 项符合「Variance」的源代码
代码结果 2,271
www.eeworm.com/read/353268/10458638
cs gaussiantransform.cs
using System;
using System.Collections.Generic;
using System.Text;
using System.IO;
namespace SVM
{
///
/// A transform which learns the mean and variance of a sample set an
www.eeworm.com/read/299984/7140028
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/460435/7250503
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/451547/7462011
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/450608/7480138
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/448535/7531494
m genardat.m
function x = genardat(a,sigma,N)
%
% Generate N points of AR data with a = [a(1) a(2), \ldots, a(n)]'
% and input variance sigma^2
%
% function x = genardat(a,sigma,N)
%
% a = AR parameters
%
www.eeworm.com/read/441427/7670512
m sigma_tr.m
%SIGMA_TR Application of Variance propagation.
% The data are taken from Example 9.2 in
% Kai Borre (1995): GPS i landmaalingen
%Written by Kai Borre
%Copyright (c) by Kai Borre
www.eeworm.com/read/441245/7672711
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/397477/8043455
m modkurt.m
function [chm, snrk] = modkurt(ch,k,p);
% Modify the kurtosis in one step, by moving in gradient direction until
% reaching the desired kurtosis value.
% It does not affect the mean nor the variance
www.eeworm.com/read/397111/8067391
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright: