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.
标签： auto-Machine-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：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.
标签： Bishop-Pattern-Recognition-and-Ma chine-Learning
上传时间： 2020-06-10
上传用户：shancjb
machine learning
上传时间： 2015-02-05
上传用户：来茴
Pascal Programs Printed in GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION, AND MACHINE LEARNING by David E. Goldberg
标签： OPTIMIZATION ALGORITHMS LEARNING Programs
上传时间： 2015-04-19
上传用户：
Machine Learning with WEKA: An Introduction （讲义） 关于数据挖掘和机器学习的.
标签： Introduction Learning Machine with
上传时间： 2013-12-27
上传用户：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.
标签： decision cross-validation estimation bootstrap
上传时间： 2015-07-26
上传用户：赵云兴
算法实现:Jieping Ye. Generalized low rank approximations of matrices. Machine Learning, Vol. 61, pp. 167-191, 2005.
标签： approximations Generalized Learning matrices
上传时间： 2015-08-29
上传用户：invtnewer
介绍随机森林(Random Forest)最早的,最经典文献!LEO BREIMAN.Random Forests.Machine Learning, 45, 5–32, 2001
标签： Random Learning BREIMAN Forests
上传时间： 2015-10-22
上传用户：stvnash
YASMET: Yet Another Small MaxEnt Toolkit (Statistical Machine Learning) 由Franz Josef Och编写，一个简短但非常经典的最大熵统计模型实现源码。
标签： Statistical Learning Another Machine
上传时间： 2015-11-17
上传用户：xiaodu1124
Machine Learning, Neural and Statistical Classification Editors: D. Michie, D.J. Spiegelhalter, C.C. Taylor February 17, 1994
标签： C. D. D.J. Classification
上传时间： 2015-12-14
上传用户：日光微澜
