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机器学习中的密度比估计

Density Ratio Estimation in Machine Learning
课程网址: http://videolectures.net/bbci2012_sugiyama_machine_learning/  
主讲教师: Masashi Sugiyama
开课单位: 视频讲座网
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
在统计机器学习中,避免密度估计是必要的,因为它通常比解决目标机器学习问题本身更困难。这通常被称为Vapnik原理,而支持向量机就是这一原理的成功实现之一。基于这一精神,提出了一种新的基于概率密度函数比值的机器学习框架。该密度比框架包括各种重要的机器学习任务,如转移学习、异常值检测、特征选择、聚类和条件密度估计。在不进行密度估计的情况下,直接估计密度比,可以有效、高效地统一解决这些问题。在本课中,我将对密度比估计的理论、算法和应用进行概述。
课程简介: In statistical machine learning, avoiding density estimation is essential because it is often more difficult than solving a target machine learning problem itself. This is often referred to as Vapnik's principle, and the support vector machine is one of the successful realizations of this principle. Following this spirit, a new machine learning framework based on the ratio of probability density functions has been introduced recently. This density-ratio framework includes various important machine learning tasks such as transfer learning, outlier detection, feature selection, clustering, and conditional density estimation. All these tasks can be effectively and efficiently solved in a unified manner by direct estimating the density ratio without going through density estimation. In this lecture, I give an overview of theory, algorithms, and application of density ratio estimation.
关 键 词: 密度估计; 支持向量机; 概率密度函数
课程来源: 信息不详。欢迎您在右侧留言补充。
入库时间: 2017-03-23
最后编审: 2017-03-23:sheny
阅读次数: 213