In this paper we propose a Gaussian-kernel-based online kernel density estimation which can be used for applications of online probability density estimation and online learning. ...
— Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian mod...
In this paper, we propose a new method, Parametric Embedding (PE), for visualizing the posteriors estimated over a mixture model. PE simultaneously embeds both objects and their c...
Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean S...
We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studi...
— This paper describes the hardware architecture for a flexible probability density estimation unit to be used in a Large Vocabulary Speech Recognition System, and targeted for m...