We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning al...
Robert H. Oehmke, Janis Hardwick, Quentin F. Stout
Abstract: Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about com...
Stefan Schaal, Christopher G. Atkeson, Sethu Vijay...
Large-scale process variations can significantly limit the practical utility of microelectro-mechanical systems (MEMS) for RF (radio frequency) applications. In this paper we desc...
Fa Wang, Gokce Keskin, Andrew Phelps, Jonathan Rot...
—With the exponential growth in the amount of data that is being generated in recent years, there is a pressing need for applying machine learning algorithms to large data sets. ...
An system-level power management technique for massively distributed wireless microsensor networks is proposed. A power aware sensor node model is introduced which enables the embe...