We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that e...
We present a novel approach for structure prediction that addresses the difficulty of obtaining labeled structures for training. We observe that structured output problems often h...
Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, Da...
In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principl...
In this paper, we propose a post randomization technique to learn a Bayesian network (BN) from distributed heterogeneous data, in a privacy sensitive fashion. In this case, two or ...
- This work investigates bandwidth learning algorithms in a version of a distributed heterogeneous data dissemination system called the Agile Information Control Environment (AICE)...