代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/400577/11572703
m parallel.m
%PARALLEL Combining classifiers in different feature spaces
%
% WC = PARALLEL(W1,W2,W3, ....) or WC = [W1;W2;W3; ...]
% WC = PARALLEL({W1;W2;W3; ...}) or WC = [{W1;W2;W3; ...}]
% WC = PARALL
www.eeworm.com/read/400577/11572969
m rejectc.m
%REJECTC Construction of a rejecting classifier
%
% WR = REJECTC(A,W,FRAC,TYPE)
%
% INPUT
% A Dataset
% W Trained or untrained classifier
% FRAC Fraction to be rejected. Def
www.eeworm.com/read/400577/11573006
m nusvc.m
%NUSVC Support Vector Classifier: NU algorithm
%
% [W,J] = NUSVC(A,KERNEL,NU)
% [W,J] = NUSVC(A,TYPE,PAR,NU)
% W = A*SVC([],KERNEL,NU)
% W = A*SVC([],TYPE,PAR,NU)
%
% INPUT
% A
www.eeworm.com/read/400577/11573018
m wvotec.m
%WVOTEC Weighted combiner (Adaboost weights)
%
% W = WVOTEC(A,V) compute weigths and store
% W = WVOTEC(V,U) Construct weighted combiner using weights U
%
% INPUT
% A Labeled data
www.eeworm.com/read/400577/11573374
m traincc.m
%TRAINCC Train combining classifier if needed
%
% W = TRAINCC(A,W,CCLASSF)
%
% INPUT
% A Training dataset
% W A set of classifiers to be combined
% CCLASSF Combining classif
www.eeworm.com/read/400577/11573381
m fisherc.m
%FISHERC Fisher's Least Square Linear Classifier
%
% W = FISHERC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Fisher's linear classifier
%
% DESCRIPTION
% Finds the linear discriminant functio
www.eeworm.com/read/400577/11573440
m testcost.m
function e = testcost(x,w,C,lablist)
%TESTCOST compute the error using the cost matrix C
%
% E = TESTCOST(A,W,C,LABLIST)
% E = TESTCOST(A*W,C,LABLIST)
% E = A*W*TESTCOST([],C,LABLIST)
%
%
www.eeworm.com/read/400576/11573467
m isocc.m
%ISOCC True for one-class classifiers
%
% isocc(w) returns true if the classifier w is a one-class classifier,
% outputting only classes 'target' and/or 'outlier' and having a
% structure with thr
www.eeworm.com/read/400576/11573478
m dd_roc.m
function [e, thr] = dd_roc(a,w)
%DD_ROC Receiver Operating Characteristic curve
%
% E = DD_ROC(A,W)
% E = DD_ROC(A*W)
% E = A*W*DD_ROC
%
% Find for a (data description) method W
www.eeworm.com/read/400576/11573510
m dd_ex3.m
% DD_EX3
%
% Show the use of the ksvdd: the support vector data description using
% several different kernels.
%
% To be honest, the SVDD is the most useful using the RBF kernel. In
% most case