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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www.eeworm.com/read/300368/13917521

java filteredclassifier.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/300368/13917572

java bvdecompose.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/133880/14019160

txt readme.txt

================================================ Fuzzy Extention for WEKA-3-4 Version: 1.0 Authors: Frank Weber [frankwe_ber@web.de] Robin Senge [senge@5th-row.com] This is an open source
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www.eeworm.com/read/204456/15339268

m stump_dd.m

%STUMP_DD Threshold one dim. one-class classifier % % W = STUMP_DD(A,FRACREJ,DIM) % % Put a threshold on one of the feature dimensions DIM of dataset A. The % threshold is put such that a frac
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www.eeworm.com/read/204456/15339287

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
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www.eeworm.com/read/108886/15570888

txt see5sam.txt

To run See5Sam.exe from a command prompt window: * Make sure that you have run See5 on your application to construct the kind of classifier that you want to use. * Put See5Sam.exe in the
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www.eeworm.com/read/108752/15577589

txt see5sam.txt

修改见http://www.rulequest.com/see5-win.html算法说明 To run See5Sam.exe from a command prompt window: * Make sure that you have run See5 on your application to construct the kind of classifier tha
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www.eeworm.com/read/143706/12850015

m wekaclassify.m

function [Y_compute, Y_prob] = WekaClassify(classifier, para, X_train, Y_train, X_test, Y_test, num_class) global temp_train_file temp_test_file temp_output_file temp_model_file; [class_set,
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www.eeworm.com/read/188848/8510936

m osusvmdemo.m

% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations--- % % 1) Demonstrations of using C-SVM Classifers. % 2) Demonstrations of using u-SVM Classifiers % 3) Demonstration
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www.eeworm.com/read/289334/8558639

m weaklearner_fast.m

function model = weaklearner_fast(data) % WEAKLEARNER Produce classifier thresholding single feature. % % Synopsis: % model = weaklearner(data) % % Description: % This function produce a weak binary
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