代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

代码结果 4,824
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java checkclassifier.java

/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either vers
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www.eeworm.com/read/124570/14558575

java evaluationutils.java

/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either vers
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www.eeworm.com/read/124570/14559689

java distributedserver.java

/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either vers
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www.eeworm.com/read/213492/15133232

m contents.m

% Bayesian classification. % % bayescls - Bayesian classifier with reject option. % bayesdf - Computes decision boundary of Bayesian classifier. % bayeserr - Computes Bayesian risk for 1D case with G
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m contents.m

% Visualization for pattern recognition. % % pandr - Visualizes solution of the Generalized Anderson's task. % pboundary - Plots decision boundary of given classifier in 2D. % pgauss
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m pandr.m

function varargout = pandr(model,distrib) % PANDR Visualizes solution of the Generalized Anderson's task. % % Synopsis: % h = pandr(model) % % Description: % It vizualizes solution of the Gen
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www.eeworm.com/read/213492/15133661

m svmclass.m

function [y,dfce] = svmclass(X,model) % SVMCLASS Support Vector Machines Classifier. % % Synopsis: % [y,dfce] = svmclass( X, model ) % % Description: % [y,dfce] = svmclass( X, model ) classifies inp
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www.eeworm.com/read/213240/15139956

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
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www.eeworm.com/read/213240/15139967

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
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www.eeworm.com/read/213240/15139999

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
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