MKL的正则化策略和贝叶斯经验学习][Regularization Strategies and Empirical Bayesian Learning for MKL]

MKL的正则化策略和贝叶斯经验学习 Regularization Strategies and Empirical Bayesian Learning for MKL


课程网址:	http://videolectures.net/nipsworkshops2010_tomioka_rse/
主讲教师:	Ryota Tomioka
开课单位:	芝加哥丰田技术学院
开课时间:	2011-01-12
课程语种:	英语
中文简介:	多核学习（MKL）近年来受到了广泛的关注。在本文中，我们展示了如何将不同的MKL算法理解为核权上不同类型正则化的应用。在本文考虑的正则化观点中，基于Tikhonov正则化的mkl公式允许我们考虑mkl后面的生成概率模型。在此模型的基础上，提出了基于边缘化似然最大化的核权学习算法。
课程简介:	Multiple kernel learning (MKL) has received considerable attention recently. In this paper, we show how different MKL algorithms can be understood as applications of different types of regularization on the kernel weights. Within the regularization view we consider in this paper, the Tikhonov-regularization-based formulation of MKL allows us to consider a generative probabilistic model behind MKL. Based on this model, we propose learning algorithms for the kernel weights through the maximization of marginalized likelihood.
关键词:	多核学习; 正则化; 概率模型; 内核权重
课程来源:	视频讲座网
最后编审:	2020-07-06：heyf
阅读次数:	51

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