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/418695/10935497
m reject.m
%REJECT Compute error-reject trade-off curve
%
% e = reject(D)
%
% Computes the error-reject curve of the classification result
% D = A*W, in which A is a dataset and W a classifier. e is a
% set
www.eeworm.com/read/299984/7140002
m svc.m
%SVC Support Vector Classifier
%
% [W,J] = SVC(A,KERNEL,C)
% [W,J] = SVC(A,TYPE,PAR,C)
% W = A*SVC([],KERNEL,C)
% W = A*SVC([],TYPE,PAR,C)
%
% INPUT
% A Dataset
% KERNEL - Un
www.eeworm.com/read/460435/7250477
m svc.m
%SVC Support Vector Classifier
%
% [W,J] = SVC(A,KERNEL,C)
% [W,J] = SVC(A,TYPE,PAR,C)
% W = A*SVC([],KERNEL,C)
% W = A*SVC([],TYPE,PAR,C)
%
% INPUT
% A Dataset
% KERNEL - Un
www.eeworm.com/read/451547/7461885
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/451547/7461931
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/441245/7672683
m svc.m
%SVC Support Vector Classifier
%
% [W,J] = SVC(A,KERNEL,C)
% [W,J] = SVC(A,TYPE,PAR,C)
% W = A*SVC([],KERNEL,C)
% W = A*SVC([],TYPE,PAR,C)
%
% INPUT
% A Dataset
% KERNEL - Un
www.eeworm.com/read/439468/7708203
m mil_train_test_validate.m
% Input pararmeter:
% D: data array, including the feature data and output class
% outputfile: the output file name of classifiers
function run = MIL_Train_Test_Validate(data_file, classifier_wrap
www.eeworm.com/read/299459/7850455
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/299459/7850926
m nearest.m
% The nearest neighbor classifier with the features extracted by PCA,
% 2D-PCA, LDA AND 2D-LDA.
% 2DPCA,then return a matrix contined all the data
p=120; % the output number of p
www.eeworm.com/read/397102/8068064
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