Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area h...
—Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance betw...
Zhengqiao Ji, Q. M. Jonathan Wu, Maher A. Sid-Ahme...
In this paper we argue that maximum expected utility is a suitable framework for modeling a broad range of decision problems arising in pattern recognition and related fields. Exa...
Modern query optimizers select an efficient join ordering for a physical execution plan based essentially on the average join selectivity factors among the referenced tables. In ...
In this paper, we discuss several facets of optimization in cloud computing, the corresponding challenges and propose an architecture for addressing those challenges. We consider ...
Marin Litoiu, C. Murray Woodside, Johnny Wong, Joa...