代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/314653/13562754
m prex_plotc.m
%PREX_PLOTC PRTools example on the dataset scatter and classifier plot
help prex_plotc
echo on
% Generate Higleyman data
A = gendath([100 100]);
% Split the data into the
www.eeworm.com/read/493294/6400251
m parzendc.m
%PARZENDC Parzen density based classifier
%
% [W,H] = PARZENDC(A)
% W = PARZENDC(A,H)
%
% INPUT
% A Dataset
% H Smoothing parameters (optional; default: estimated from A for each class)
www.eeworm.com/read/493294/6400319
m clevals.m
%CLEVALS Classifier evaluation (feature size/learning curve), bootstrap possible
%
% E = CLEVALS(A,CLASSF,FEATSIZE,TRAINSIZES,NREPS,T,FID)
%
% INPUT
% A Training dataset
% CLASSF Cl
www.eeworm.com/read/493294/6400554
m prex_plotc.m
%PREX_PLOTC PRTools example on the dataset scatter and classifier plot
help prex_plotc
echo on
% Generate Higleyman data
A = gendath([100 100]);
% Split the data into the
www.eeworm.com/read/480105/6676794
m sampling.m
function [Itrain , Itest , Ivalid] = sampling(X , y , options);
% Various Data sampling methods for evaluate Classifier Performances.
%
% X : data (d x N)
% y
www.eeworm.com/read/264146/11327619
m coverage.m
function Coverage=coverage(Outputs,test_target)
%Computing the coverage
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Outputs(j,i)
www.eeworm.com/read/400577/11572981
m parzendc.m
%PARZENDC Parzen density based classifier
%
% [W,H] = PARZENDC(A)
% W = PARZENDC(A,H)
%
% INPUT
% A Dataset
% H Smoothing parameters (optional; default: estimated from A for each class)
www.eeworm.com/read/400577/11573009
m naivebc.m
%NAIVEBC Naive Bayes classifier
%
% W = NAIVEBC(A,N)
% W = A*NAIVEBC([],N)
%
% W = NAIVEBC(A,DENSMAP)
% W = A*NAIVEBC([],DENSMAP)
%
% INPUT
% A Training dataset
% N Scalar numbe
www.eeworm.com/read/400577/11573439
m prex_plotc.m
%PREX_PLOTC PRTools example on the dataset scatter and classifier plot
help prex_plotc
echo on
% Generate Higleyman data
A = gendath([100 100]);
% Split the data into the
www.eeworm.com/read/256799/11971855
m parzendc.m
%PARZENDC Parzen density based classifier
%
% [W,H] = PARZENDC(A)
% W = PARZENDC(A,H)
%
% INPUT
% A Dataset
% H Smoothing parameters (optional; default: estimated from A for each class)