The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
標(biāo)簽： Auto-machine-learning-methods-Sys tems-Challenges
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
標(biāo)簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
machine learning
上傳時(shí)間： 2015-02-05
上傳用戶：來茴
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，一個(gè)簡短但非常經(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
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