代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/418695/10935265
m rsubc.m
%RSUBC Random Subspace Classifier
%
% W = rsubc(A,classf,r,n,cclassf,T)
%
% Computation of a combined classifier by selecting n random subsets
% of r features. For each of these subsets the base c
www.eeworm.com/read/418695/10935514
m getclass.m
%GETCLASS Get classifier bit of mapping
function classbit = getclass(w)
classbit = w.s;
return
www.eeworm.com/read/418695/10935555
m isclassifier.m
%ISCLASSIFIER Get classifier bit of mapping
function classbit = isclassifier(w)
classbit = w.s;
return
www.eeworm.com/read/418695/10935574
m mclassc.m
%MCLASSC Computation of multi-class classifier from 2-class discriminants
%
% W = mclassc(A,classf)
%
% The untrained classifier classf is called to compute c classifiers
% between each of the c class
www.eeworm.com/read/468922/6981911
m contents.m
% This toolbox was edited by Eng.\ Alaa Tharwat Othman
% This toolbox is designed to use into pattern recognition systems (specially for images)
% This code is edited by Eng. Alaa Tharwat Abd El. Mo
www.eeworm.com/read/468922/6981930
txt readme.txt
% This toolbox was edited by Eng.\ Alaa Tharwat Othman
% This toolbox is designed to use into pattern recognition systems (specially for images)
% This code is edited by Eng. Alaa Tharwat Abd El. Mo
www.eeworm.com/read/466591/7029497
m svmclass.m
function [y,dfce] = svmclass(X,model)
% SVMCLASS Support Vector Machines Classifier.
%
% Synopsis:
% [y,dfce] = svmclass( X, model )
%
% Description:
% [y,dfce] = svmclass( X, model ) classifies inp
www.eeworm.com/read/463439/7109483
nbc 2line.nbc
/*----------------------------------------------------------------------
domains
----------------------------------------------------------------------*/
dom(X) = IR;
dom(Y) = IR;
dom(C) = { a, b };
www.eeworm.com/read/299984/7139975
m loglc.m
%LOGLC Logistic Linear Classifier
%
% W = LOGLC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Logistic linear classifier
%
% DESCRIPTION
% Computation of the linear classifier for the dataset
www.eeworm.com/read/299984/7139979
m baggingc.m
%BAGGINGC Bootstrapping and aggregation of classifiers
%
% W = BAGGINGC (A,CLASSF,N,ACLASSF,T)
%
% INPUT
% A Training dataset.
% CLASSF The base classifier (default: nmc)
% N