On-line estimation with the multivariate Gaussian distribution

被引：11

作者：

Dasgupta, Sanjoy ^{[1
]}

Hsu, Daniel ^{[1
]}

机构：

[1] Univ Calif San Diego, San Diego, CA 92103 USA

来源：

LEARNING THEORY, PROCEEDINGS | 2007年 / 4539卷

关键词：

D O I：

10.1007/978-3-540-72927-3_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider on-line density estimation with the multivariate Gaussian distribution. In each of a sequence of trials, the learner must posit a mean mu and covariance Sigma; the learner then receives an instance x and incurs loss equal to the negative log-likelihood of x under the Gaussian density parameterized by (mu, Sigma). We prove bounds on the regret for the follow-the-leader strategy, which amounts to choosing the sample mean and covariance of the previously seen data.

引用

页码：278 / +

页数：2

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