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/394727/8210411
m knnr.m
function [computedOutput, combinedComputedOutput, nearestIndex, knnrMat] = knnr(DS, TS, k)
% knnr: K-nearest neighbor rule for classification
% Usage:
% [computedOutput, combinedComputedOutput, nea
www.eeworm.com/read/411674/11233773
m contents.m
% Miscellaneous functions for STPRtoolbox.
%
% adaboost - AdaBoost algorithm.
% adaclass - AdaBoost classifier.
% cerror - Computes classification error.
% crossval - Partions data
www.eeworm.com/read/147693/12538662
pl prune_tree.pl
% Solution to Exercise 18.6
% prunetree( Tree, PrunedTree): PrunedTree is optimally pruned Tree
% with respect to estimated classification error using Laplace estimate
% Assume trees are bi
www.eeworm.com/read/111603/15509323
m getkernel.m
function kernel = getkernel(net)
% GETKERNEL
%
% Accessor method returning the kernel used in a support vector classification
% network.
%
% ker = getkernel(net)
%
% File : @svc/
www.eeworm.com/read/289680/8534982
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 : @svc/
www.eeworm.com/read/289680/8534989
m strip.m
function net = strip(net, tolerance)
% STRIP
%
% Delete support vectors from a support vector classification network for which
% the magnitude of the corresponding weight is less than a given to
www.eeworm.com/read/289680/8535161
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/188280/8552119
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 : @svc/
www.eeworm.com/read/188280/8552127
m strip.m
function net = strip(net, tolerance)
% STRIP
%
% Delete support vectors from a support vector classification network for which
% the magnitude of the corresponding weight is less than a given to
www.eeworm.com/read/188280/8552310
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