A novel technique for maximum "a posteriori" (MAP) adaptation of maximum entropy (MaxEnt) and maximum entropy Markov models (MEMM) is presented. The technique is applied...
—Using high-rate theory approximations we introduce flexible practical quantizers based on possibly non-Gaussian models in both the constrained resolution (CR) and the constrain...
Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are "near-neighbors" of...
Cardinality estimation is the problem of estimating the number of tuples returned by a query; it is a fundamentally important task in data management, used in query optimization, ...
Abstract. We consider the problem of estimating an unknown probability distribution from samples using the principle of maximum entropy (maxent). To alleviate overfitting with a v...