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/431675/8662460
m testd.m
%TESTD Classification error estimate
%
% [e,j,k,l] = testd(A,W,r,iter)
%
% Test of dataset A on the classifier defined by W. Returns:
% e - the fraction of A that is incorrectly classified by W.
%
www.eeworm.com/read/386050/8767478
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer
% S Standard deviation of weights in an input layer (default: 1
www.eeworm.com/read/386050/8768269
m mogc.m
%MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
%
% INPUT
% A Dataset
% N Number of mixtures (optional; default 2)
% R,S Regularization parameters, 0
www.eeworm.com/read/386050/8768950
m lssvc.m
function W = lssvc(A, TYPE, PAR, C)
%LSSVC Least-Squares Support Vector Classifier
%
% W = lssvc(A,TYPE,PAR,C);
%
% INPUT
% A dataset
% TYPE Type of the kernel (optional; default: '
www.eeworm.com/read/386050/8769159
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/428849/8834556
m quadclass.m
function [y,dfce]=quadclass( X, model)
% QUADCLASS Quadratic classifier.
%
% Synopsis:
% [y,dfce] = quadclass(X,model)
%
% Description:
% This function classifies input data X using quadratic
% dis
www.eeworm.com/read/428849/8834669
m~ rspoly2.m~
function red_model = redquadh(model)
% REDQUADH reduced SVM classifier with homogeneous quadratic kernel.
%
% Synopsis:
% red_model = redquadh(model)
%
% Description:
% It uses reduced set techique
www.eeworm.com/read/428849/8834754
m redquadh.m
function red_model = redquadh(model)
% REDQUADH reduced SVM classifier with homogeneous quadratic kernel.
%
% Synopsis:
% red_model = redquadh(model)
%
% Description:
% It uses reduced set techique
www.eeworm.com/read/428849/8834817
m tune_ocr.m
% TUNE_OCR Tunes SVM classifier for OCR problem.
%
% Description:
% The following steps are performed:
% - Training set is created from data in directory ExamplesDir.
% - Multi-class SVM is
www.eeworm.com/read/426679/9004401
m nnclassifier.m
% Nearest Neighbour Classifier-NNC
function [NNCrate]=NNclassifier(features,test_features,trnum,tenum,classnum)
% features the matrix that training samples projected on feature subspace(训练样本