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/397106/8067550
m lvq3_vc.m
% Learns classifier and classifies test set
% using Learning Vector Quantization algorithm nr 3
% Usage
% [trainError, testError, estTrainLabels, estTestLabels] = ...
% LVQ3_VC(trainFea
www.eeworm.com/read/397106/8067771
m lvq1_vc.m
% Learns classifier and classifies test set
% using Learning Vector Quantization algorithm nr 1
% Usage
% [trainError, testError, estTrainLabels, estTestLabels] = ...
% LVQ1_VC(trainFea
www.eeworm.com/read/397102/8068008
m polyc.m
%POLYC Polynomial Classification
%
% W = polyc(A,classf,n,s)
%
% Adds polynomial features to the dataset A and runs the untrained
% classifier classf. n is the degree of the polynome (default 1).
www.eeworm.com/read/397102/8068036
m kljlc.m
%KLJLC Linear classifier using KL expansion on the joint data.
%
% W = kljlc(A,n)
%
% Finds the linear discriminant function W for the dataset A
% computing the ldc on a projection of the data on
www.eeworm.com/read/137160/13342258
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/137160/13342344
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/137160/13342696
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/320830/13417573
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/314653/13562514
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/314653/13562559
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