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/460435/7250508
m contents.m
% Pattern Recognition Tools
% Version 4.1.3 10-Jun-2008
%
%Datasets and Mappings (just most important routines)
%---------------------
%dataset Define dataset from datamatrix and labels
%datasets
www.eeworm.com/read/460435/7250814
m meanc.m
%MEANC Mean combining classifier
%
% W = MEANC(V)
% W = V*MEANC
%
% INPUT
% V Set of classifiers (optional)
%
% OUTPUT
% W Mean combiner
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a s
www.eeworm.com/read/460435/7251017
m klldc.m
%KLLDC Linear classifier built on the KL expansion of the common covariance matrix
%
% W = KLLDC(A,N)
% W = KLLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of significant eigenvectors
% AL
www.eeworm.com/read/460435/7251024
m tree_map.m
%TREE_MAP Map a dataset by binary decision tree
%
% F = TREE_MAP(A,W)
%
% INPUT
% A Dataset
% W Decision tree mapping
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset
www.eeworm.com/read/460435/7251167
m testn.m
%TESTN Error estimate of discriminant for normal distribution.
%
% E = TESTN(W,U,G,N)
%
% INPUT
% W Trained classifier mapping
% U C x K dataset with C class means, labels and priors (default
www.eeworm.com/read/460435/7251181
m prtestc.m
%PRTESTC Test routine for the PRTOOLS classifier
%
% This script tests a given, untrained classifier w, defined in the
% workspace, e.g. w = my_classifier. The goal is to find out whether
% w fulfill
www.eeworm.com/read/460435/7251186
m prtools.m
% Pattern Recognition Tools
% Version 4.1.3 10-Jun-2008
%
%Datasets and Mappings (just most important routines)
%---------------------
%dataset Define dataset from datamatrix and labels
%datasets
www.eeworm.com/read/451547/7461878
m dd_fp.m
function e = dd_fp(w,z,err)
%DD_FP
%
% E = DD_FP(W,Z,ERR)
%
% Change the threshold of a (trained) classifier W, such that the error
% on the target class (the fraction false negative) is set to ERR
www.eeworm.com/read/451547/7461980
m multic.m
%MULTIC Make a multi-class classifier
%
% W = MULTIC(A,V)
%
% Train the (untrained!) one-class classifier V on each of the classes
% in A, and combine it to a multi-class classifier W. If an object
www.eeworm.com/read/451547/7462007
m p_map.m
%PARZEN_MAP Map a dataset on a Parzen densities based classifier
%
% F = p_map(A,W)
%
% Maps the dataset A by the Parzen density based classfier W. It
% outputs just the raw class probabilities (i.