代码搜索：One-Class
找到约 293 项符合「One-Class」的源代码
代码结果 293
www.eeworm.com/read/441245/7673394
m gencirc.m
%GENCIRC Generation of a one-class circular dataset
%
% A = GENCIRC(N,S)
%
% INPUT
% N Size of dataset (optional; default: 50)
% S Standard deviation (optional; default: 0.1)
%
% OUTPUT
%
www.eeworm.com/read/397111/8067098
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/397111/8067195
m random_dd.m
%RANDOM_DD Random one-class classifier
%
% W = RANDOM_DD(A,FRACREJ)
%
% This is the trivial one-class classifier, randomly assigning labels
% and rejecting FRACREJ of the data objects. This pr
www.eeworm.com/read/397111/8067201
m contents.m
% Data Description Toolbox
% Version 1.11 23-Nov-2003
%
%Dataset construction
%--------------------
%isocset true if dataset is one-class dataset
%gendatoc generate a one-class dataset fr
www.eeworm.com/read/397097/8069130
m random_dd.m
%RANDOM_DD Random one-class classifier
%
% W = random_dd(A,fracrej)
%
% This is the trivial one-class classifier, randomly assigning labels
% and rejecting fracrej of the data objects. This pr
www.eeworm.com/read/137160/13342591
m gencirc.m
%GENCIRC Generation of a one-class circular dataset
%
% A = GENCIRC(N,S)
%
% INPUT
% N Size of dataset (optional; default: 50)
% S Standard deviation (optional; default: 0.1)
%
% OUTPUT
%
www.eeworm.com/read/314653/13562696
m gencirc.m
%GENCIRC Generation of a one-class circular dataset
%
% A = GENCIRC(N,S)
%
% INPUT
% N Size of dataset (optional; default: 50)
% S Standard deviation (optional; default: 0.1)
%
% OUTPUT
%
www.eeworm.com/read/493294/6399990
m random_dd.m
%RANDOM_DD Random one-class classifier
%
% W = RANDOM_DD(A,FRACREJ)
%
% This is the trivial one-class classifier, randomly assigning labels
% and rejecting FRACREJ of the data objects. This pr
www.eeworm.com/read/493294/6400477
m gencirc.m
%GENCIRC Generation of a one-class circular dataset
%
% A = GENCIRC(N,S)
%
% INPUT
% N Size of dataset (optional; default: 50)
% S Standard deviation (optional; default: 0.1)
%
% OUTPUT
%
www.eeworm.com/read/492400/6422247
m random_dd.m
%RANDOM_DD Random one-class classifier
%
% W = RANDOM_DD(A,FRACREJ)
%
% This is the trivial one-class classifier, randomly assigning labels
% and rejecting FRACREJ of the data objects. This pr