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/344640/11870075
m u_rbfdemo.m
echo off
% RBFDEMO demonstration for using nonlinear SVM classifier
% with a RBF kernel.
echo on;
clc
% RBFDEMO demonstration for using nonlinear SVM classifier
% with a RBF kernel.
%#####
www.eeworm.com/read/344640/11870082
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/256798/11971866
m knn_map.m
%KNN_MAP Map a dataset on a K-NN classifier
%
% F = KNN_MAP(A,W)
%
% INPUT
% A Dataset
% W k-NN classifier trained by KNNC
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps t
www.eeworm.com/read/342711/12005117
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/342008/12046789
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/342008/12046840
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/342008/12046852
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/342008/12046871
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/342008/12046971
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
www.eeworm.com/read/342008/12046989
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