代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

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xml introduction.xml

Introduction Select is a tool for performing and evaluating email classification using supervised learning methods with in
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www.eeworm.com/read/128468/14295621

m linclass.m

function [Ipred, Fx ]=linclass(X,alpha,theta) % LINCLASS classifier based on linear discriminat function. % [Ipred, Fx ]=linclass(X,alpha,theta) % % LINCLASS is the classifier based on a linear d
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www.eeworm.com/read/124570/14558474

java iterativeclassifier.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/124570/14558546

java metacost.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/124570/14558780

java evaluation.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/124570/14558846

java evaluationclient.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/119681/14824470

m roc.m

function [AREA,SE,RESULT_S,FPR_ROC,TPR_ROC,TNa,TPa,FNa,FPa]=roc(RESULT,CLASS,fig) % Receiver Operating Characteristic (ROC) curve of a binary classifier % % >> [area, se, deltab, oneMinusSpec, sen
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www.eeworm.com/read/214923/15082988

m roc.m

function [AREA,SE,RESULT_S,FPR_ROC,TPR_ROC,TNa,TPa,FNa,FPa]=roc(RESULT,CLASS,fig) % Receiver Operating Characteristic (ROC) curve of a binary classifier % % >> [area, se, deltab, oneMinusSpec, sen
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www.eeworm.com/read/213240/15139940

m dd_fp.m

function e = dd_fp(w,z,err) %DD_FP % % E = DD_FP(W,Z,ERR) % % Change the threshold of a (trained) classifier W, such that the error % on the target class (the fraction false negative) is set to ERR
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www.eeworm.com/read/213240/15140067

m p_map.m

%PARZEN_MAP Map a dataset on a Parzen densities based classifier % % F = p_map(A,W) % % Maps the dataset A by the Parzen density based classfier W. It % outputs just the raw class probabilities (i.
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