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/421949/10675986
m u_lindemo.m
echo off
%LINDEMO demonstration for using linear SVM classifier.
echo on;
clc
%LINDEMO demonstration for using linear SVM classifier.
%#########################################################
www.eeworm.com/read/421949/10675988
m c_clademo.m
echo off
% CLADEMO demonstration for using a contructed SVM classifier to classify
% input patterns
echo on;
%
%
% NOTICE: please first run any of the first three demonstrations before
%
www.eeworm.com/read/421949/10676010
m svmclass.m
function [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias, Parameters, nSV, nLabel)
% Usages:
% [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias);
% [Labels, DecisionValu
www.eeworm.com/read/349725/10802033
m svmclass.m
function [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias, Parameters, nSV, nLabel)
% Usages:
% [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias);
% [Labels, DecisionValu
www.eeworm.com/read/418756/10928173
m adademo.m
function MOV=adademo
% ADADEMO AdaBoost demo
% ADADEMO runs AdaBoost on a simple two dimensional classification
% problem.
% Written by Andrea Vedaldi - 2006
% http://vision.ucla.edu/~vedaldi
do_
www.eeworm.com/read/418695/10935170
m classd.m
%CLASSD Classify data using a given classifier
%
% labels = classd(D)
%
% Finds the labels of the classified dataset D (typically the result
% of a mapping or classification A*W). For each object
www.eeworm.com/read/418695/10935190
m baggingc.m
%BAGGINGC Bootstrapping and aggregation of classifiers
%
% W = baggingc(A,classf,n,cclassf,T)
%
% Computation of a stabilized version of a classifier by
% bootstrapping and aggregation ('bagging
www.eeworm.com/read/418695/10935194
m rbnc.m
%RBNC Radial basis neural net classifier
%
% W = rbnc(A,n)
%
% A feedforward neural network classifier with one hidden layer with
% at most n radial basis units is computed for the labeled dataset
www.eeworm.com/read/418695/10935205
m knnc.m
%KNNC K-Nearest Neighbor Classifier
%
% [W,k,e] = knnc(A,k)
%
% Computation of the k-nearest neigbor classifier for the dataset A.
% Default k: optimize leave-one-out error e. W is a mapping and
%
www.eeworm.com/read/418695/10935254
m parzenc.m
%PARZENC Optimisation of the Parzen classifier
%
% [W,h,e] = parzenc(A)
%
% Computation of the optimum smoothing parameter h for the Parzen
% classifier between the classes in the dataset A. The l