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/428849/8834646
m knnclass.m
function y = knnclass(X,model)
% KNNCLASS k-Nearest Neighbours classifier.
%
% Synopsis:
% y = knnclass(X,model)
%
% Description:
% The input feature vectors X are classified using the K-NN
% rule
www.eeworm.com/read/375500/9358474
h bfagent.h
// Interface for BFagent -- Classifier predictors
#import "Agent.h"
#import "BFParams.h"
#import "BFCast.h"
#import
#import "World.h"
//pj: // Structure for list of indiv
www.eeworm.com/read/362246/10010122
m knnclass.m
function y = knnclass(X,model)
% KNNCLASS k-Nearest Neighbours classifier.
%
% Synopsis:
% y = knnclass(X,model)
%
% Description:
% The input feature vectors X are classified using the K-NN
% rule
www.eeworm.com/read/360995/10069861
m lpdd.m
%LPDD Linear programming distance data description
%
% W = LPDD(X,NU,S,DTYPE,P)
%
% One-class classifier put into a linear programming framework. From
% the data X the distance matrix is comp
www.eeworm.com/read/360995/10069948
m dd_normc.m
%DD_NORMC Normalize the output of a oc-classifier
%
% B = DD_NORMC(A)
% B = A*W*DD_NORMC
% W = DD_NORMC
%
% Normalize the mapped dataset A to standard 'posterior probability'
% est
www.eeworm.com/read/280595/10311902
m knnclass.m
function y = knnclass(X,model)
% KNNCLASS k-Nearest Neighbours classifier.
%
% Synopsis:
% y = knnclass(X,model)
%
% Description:
% The input feature vectors X are classified using the K-NN
% rule
www.eeworm.com/read/159921/10587730
m~ knnclass.m~
function [class] = knnclass(tst,X,I,K)
% [class] = knnclass(tst,X,I,K)
%
% KNNCLASS is an implementation of K-Nearest Neighbours
% classifier. The Euclidean distance is used.
%
% Input:
% tst [DxNt
www.eeworm.com/read/421949/10676360
m~ knnclass.m~
function [class] = knnclass(tst,X,I,K)
% [class] = knnclass(tst,X,I,K)
%
% KNNCLASS is an implementation of K-Nearest Neighbours
% classifier. The Euclidean distance is used.
%
% Input:
% tst [DxNt
www.eeworm.com/read/418695/10935267
m parsc.m
%PARSC Pars classifier
%
% parsc(w)
%
% Displays the type and, for combining classifiers, the structure of
% the mapping w.
%
% See also mappings
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
www.eeworm.com/read/418695/10935436
m roc.m
%ROC Receiver-operator curve
%
% e = roc(D,k)
%
% Computes k points of the receiver-operator curve of the classifier
% W for the labeled data set D, which is typically the result of
% D = A*W*clas