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/351797/10609871
m getnsv.m
function nsv = getnsv(net)
% GETNSV
%
% Accessor method returning the number of support vectors of a support vector
% classification network.
%
% n = getnsv(net);
%
% File : @dags
www.eeworm.com/read/273047/10930338
m getnsv.m
function nsv = getnsv(net)
% GETNSV
%
% Accessor method returning the number of support vectors of a support vector
% classification network.
%
% n = getnsv(net);
%
% File : @dags
www.eeworm.com/read/469416/6976488
m demtrain.m
function demtrain(action);
%DEMTRAIN Demonstrate training of MLP network.
%
% Description
% DEMTRAIN brings up a simple GUI to show the training of an MLP
% network on classification and regressi
www.eeworm.com/read/469123/6977828
m binarylaplacegp.m
function varargout = binaryLaplaceGP(hyper, covfunc, lik, varargin)
% binaryLaplaceGP - Laplace's approximation for binary Gaussian process
% classification. Two modes are possible: training or testi
www.eeworm.com/read/299984/7139941
m feateval.m
%FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untraine
www.eeworm.com/read/460435/7250416
m feateval.m
%FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untraine
www.eeworm.com/read/450608/7480079
m feateval.m
%FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untraine
www.eeworm.com/read/450608/7480571
m featselb.m
%FEATSELB Backward feature selection for classification
%
% [W,R] = FEATSELB(A,CRIT,K,T,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
% (opti
www.eeworm.com/read/442927/7641726
m decisionboundaryplot_b.m
function out=decisionBoundaryPlot(xx, yy, class, color)
% decisionBoundaryPlot: Plot of the decision boundary of a classification problem
% Roger Jang, 20041201
if nargin
www.eeworm.com/read/441245/7672619
m feateval.m
%FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untraine