Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiers are accurate, fast, and need no parameter tuning. We presen...
We describe a formalization of the elementary algebra, topology and analysis of finite-dimensional Euclidean space in the HOL Light theorem prover. (Euclidean space is RN with the...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Large-scale Internet applications can benefit from an ability to predict round-trip times to other hosts without having to contact them first. Explicit measurements are often un...
Frank Dabek, Russ Cox, M. Frans Kaashoek, Robert M...
The present work studies clustering from an abstract point of view and investigates its properties in the framework of inductive inference. Any class S considered is given by a hyp...
John Case, Sanjay Jain, Eric Martin, Arun Sharma, ...