We consider the AdaBoost procedure for boosting weak learners. In AdaBoost, a key step is choosing a new distribution on the training examples based on the old distribution and th...
This paper presents a new non-iterative, closed-form approximation to the maximum entropy (M.E.) image restoration method. A fast frequency domain implementation of this closed fo...
Nilsson's Probabilistic Logic is a set theoretic mechanism for reasoning with uncertainty. We propose a new way of looking at the probability constraints enforced by the fram...
Abstract--In this paper, the Lagrangian formulation of variablerate vector quantization is extended to quantization with simultaneous constraints on entropy and codebook size, incl...
We present deterministic sub-linear space algorithms for a number of problems over update data streams, including, estimating frequencies of items and ranges, finding approximate ...