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A选择最最好的预算模型的选择方法

A selecting-the-best method for budgeted model selection
课程网址: http://videolectures.net/ecmlpkdd2011_bontempi_budgeted/  
主讲教师: Gianluca Bontempi
开课单位: 比利时自由大学
开课时间: 2011-11-30
课程语种: 英语
中文简介:
本文重点讨论了预算模型的选择, 即在模型评估数量与替代方法数量之比虽然大于一个时, 在一组备选模型之间的选择。我们提出了一种基于正确选择概率概念的方法, 这种概念是从蒙特卡罗随机逼近的领域借用来的。其目的是从数据中估计贪婪选择返回最佳选择的概率, 并定义最大限度地增加此类数量的采样规则。在两种备选方案的情况下, 通过使用克拉克的一组随机变量的最大值的近似值, 将分析结果推广到更多的备选方案。综合和实际模型选择任务的初步结果表明, 该技术具有竞争力的最先进的算法, 如土匪 ucb。
课程简介: The paper focuses on budgeted model selection, that is the selection between a set of alternative models when the ratio between the number of model assessments and the number of alternatives, though bigger than one, is low. We propose an approach based on the notion of probability of correct selection, a notion borrowed from the domain of Monte Carlo stochastic approximation. The idea is to estimate from data the probability that a greedy selection returns the best alternative and to define a sampling rule which maximizes such quantity. Analytical results in the case of two alternatives are extended to a larger number of alternatives by using the Clark's approximation of the maximum of a set of random variables. Preliminary results on synthetic and real model selection tasks show that the technique is competitive with state-of-the-art algorithms, like the bandit UCB.
关 键 词: 计算机科学; 机器学习; 蒙特卡罗方法
课程来源: 视频讲座网
最后编审: 2020-06-06:魏雪琼(课程编辑志愿者)
阅读次数: 54