The widely used maximum likelihood linear regression speaker adaptation procedure suffers from overtraining when used for rapid adaptation tasks in which the amount of adaptation data is severely limited. This is a well known difficulty associated with the expectation maximization algorithm. We use an information geometric analysis of the expectation maximization algorithm as an alternating min...

A litany of questions from a wide variety of scientific disciplines can be cast as non-monotone submodular maximization problems. Since this class of problems includes max-cut, it is NP-hard. Thus, general purpose algorithms for the class tend to be approximation algorithms. For unconstrained problem instances, one recent innovation in this vein includes an algorithm of Buchbinder et al. (2012)...

| We formulate the problem of approximating a signal with a sum of chirped Gaussians, the so-called chirplets, under the framework of maximum likelihood estimation. For a signal model of one chirplet in noise, we formulate the maximum likelihood estimator (MLE) and compute the Cram er-Rao lower bound. An approximate MLE is developed, based on time-frequency methods, and is applied sequentially ...

This paper treats a multiresolution hidden Markov model for classifying images. Each image is represented by feature vectors at several resolutions, which are statistically dependent as modeled by the underlying state process, a multiscale Markov mesh. Unknowns in the model are estimated by maximum likelihood, in particular by employing the expectation-maximization algorithm. An image is classi...

The problem of joint blind channel estimation and multiple access interference (MAI) suppression for an asynchronous code-division multiple-access (CDMA) system is studied. A low-complexity sliding-window scheme based on the expectation maximization (EM) algorithm is developed for joint blind maximum a posteriori probability (MAP) multi-user detection (MUD) and stochastic maximum likelihood (ML...

Many set functions F in combinatorial optimization satisfy the diminishing returns property F{A U X) — F(A) > F(A' U X) — F(A') for A C A!. Such functions are called submodular. A result from Nemhauser etal. states that the problem of selecting ^-element subsets maximizing a nondecreasing submodular function can be approximated with a constant factor (1 — 1/e) performance guarantee. Khuller eta...

We link the weighted maximum entropy and the optimization of the expected Fβmeasure, by viewing them in the framework of a general common multi-criteria optimization problem. As a result, each solution of the expected Fβ-measure maximization can be realized as a weighted maximum likelihood solution a well understood and behaved problem. The specific structure of maximum entropy models allows us...

We demonstrate the use of context features, namely, names of places, and unlabelled data for the detection of personal name language of origin. While some early work used either rule-based methods or n-gram statistical models to determine the name language of origin, we use the discriminative classification maximum entropy model and view the task as a classification task. We perform bootstrappi...

While a number of scheduling policies achieve the maximum throughput region, the average delay minimization problem for cellular broadcast systems still awaits its complete solution. To this end, we introduce a scheduling policy which decomposes the cross-layer delay optimization problem into two subproblems: allocation of physical resources and user priority management. The first subproblem is...

In their famous book, Ford and Fulkerson [ 3 ] state that the situation in which there are multiple sources and sinks, with flow permitted from any source to any sink, presents nothing new. They claim that a multiple source, multiple sink network reduces to a single source, single sink network by the adjunction of a supersource, a supersink, and several arcs. That is of course true if only a ma...