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MacHINe learning
上傳時(shí)間： 2015-02-05
上傳用戶：來(lái)茴
Pascal Programs Printed in GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION, AND MacHINe learning by David E. Goldberg
標(biāo)簽： OPTIMIZATION ALGORITHMS LEARNING Programs
上傳時(shí)間： 2015-04-19
上傳用戶：
MacHINe learning with WEKA: An Introduction （講義） 關(guān)于數(shù)據(jù)挖掘和機(jī)器學(xué)習(xí)的.
標(biāo)簽： Introduction Learning Machine with
上傳時(shí)間： 2013-12-27
上傳用戶：qq521
MacHINe learning, accuracy estimation, cross-validation, bootstrap, ID3, decision trees, decision graphs, naive-bayes, decision tables, majority, induction algorithms, classifiers, categorizers, general logic diagrams, instance-based algorithms, discretization, lazy learning, bagging, MineSet.
標(biāo)簽： decision cross-validation estimation bootstrap
上傳時(shí)間： 2015-07-26
上傳用戶：趙云興
算法實(shí)現(xiàn):Jieping Ye. Generalized low rank approximations of matrices. MacHINe learning, Vol. 61, pp. 167-191, 2005.
標(biāo)簽： approximations Generalized Learning matrices
上傳時(shí)間： 2015-08-29
上傳用戶：invtnewer
介紹隨機(jī)森林(Random Forest)最早的,最經(jīng)典文獻(xiàn)!LEO BREIMAN.Random Forests.MacHINe learning, 45, 5–32, 2001
標(biāo)簽： Random Learning BREIMAN Forests
上傳時(shí)間： 2015-10-22
上傳用戶：stvnash
YASMET: Yet Another Small MaxEnt Toolkit (Statistical MacHINe learning) 由Franz Josef Och編寫(xiě)，一個(gè)簡(jiǎn)短但非常經(jīng)典的最大熵統(tǒng)計(jì)模型實(shí)現(xiàn)源碼。
標(biāo)簽： Statistical Learning Another Machine
上傳時(shí)間： 2015-11-17
上傳用戶：xiaodu1124
MacHINe learning, Neural and Statistical Classification Editors: D. Michie, D.J. Spiegelhalter, C.C. Taylor February 17, 1994
標(biāo)簽： C. D. D.J. Classification
上傳時(shí)間： 2015-12-14
上傳用戶：日光微瀾
MacHINe learning Weka 數(shù)據(jù)變換，給Arff文件加載權(quán)值，變換為XRFF文件。
標(biāo)簽： Learning Machine Weka 數(shù)據(jù)
上傳時(shí)間： 2015-12-16
上傳用戶：wxhwjf
Feature selection is a preprocessing technique frequently used in data mining and MacHINe learning tasks. It can reduce dimensionality, remove irrelevant data, increase learning accuracy, and improve results comprehensibility. FCBF is a fast correlation-based filter algorithm designed for high-dimensional data and has been shown effective in removing both irrelevant features and redundant features
標(biāo)簽： preprocessing frequently selection technique
上傳時(shí)間： 2014-01-19
上傳用戶：lindor
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