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/181388/9256606
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a multi-class support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each colu
www.eeworm.com/read/181388/9256713
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a multi-class support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each colu
www.eeworm.com/read/181388/9256717
m fwd.m
function y = fwd(net, x)
% FWD
%
% Compute the output of a dag-svm multi-class support vector classification
% network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, in
www.eeworm.com/read/180274/9313792
h multiclass_ecoc.h
// -*- C++ -*-
#ifndef __LEMGA_MULTICLASS_ECOC_H__
#define __LEMGA_MULTICLASS_ECOC_H__
/** @file
* @brief Declare @link lemga::MultiClass_ECOC MultiClass_ECOC@endlink
* (Multiclass classification
www.eeworm.com/read/374698/9388868
m trainlssvm.m
function [model,b,X,Y] = trainlssvm(model,X,Y)
% Train the support values and the bias term of an LS-SVM for classification or function approximation
%
% >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
www.eeworm.com/read/373632/9445402
r knn.var.r
### Name: knn.var
### Title: K-Nearest Neighbor Classification With Variable Selection
### Aliases: knn.var
### Keywords: models
### ** Examples
data(iris)
set.seed (3)
samp
www.eeworm.com/read/362246/10009854
pl references.pl
{
"Anderson62" =>"T.W.Anderson and R.R.Bahadur. Classification into two
multivariate normal distributions with differrentia covariance matrices.
Anals of Mathematical Statistics, 33:420--431, Ju
www.eeworm.com/read/362246/10009872
m contents.m
% Data sets used by the STPRtool.
%
% andersons_task - (dir) Input for demo on Generalized Anderson's task.
% binary_separable - (dir) Input for demo on Linear classification.
% gmm_sample - (
www.eeworm.com/read/360895/10072673
m trainlssvm.m
function [model,b,X,Y] = trainlssvm(model,X,Y)
% Train the support values and the bias term of an LS-SVM for classification or function approximation
%
% >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
www.eeworm.com/read/280595/10311495
pl references.pl
{
"Anderson62" =>"T.W.Anderson and R.R.Bahadur. Classification into two
multivariate normal distributions with differrentia covariance matrices.
Anals of Mathematical Statistics, 33:420--431, Ju