Visualizing probabilistic models in Minkowski space with intensive symmetrized Kullback-Leibler embedding

Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality

Kullback-Leibler Boosting

In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based on the sum of histogram divergences along corresponding global and discriminating linear features. Second, these linear features, called KL features, are iteratively learnt by maximizing the projected Kullback-Leibler d...

Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models

Kullback Leibler (KL) control problems allow for efficient computation of optimal control by solving a principal eigenvector problem. However, direct applicability of such framework to continuous state-action systems is limited. In this paper, we propose to embed a KL control problem in a probabilistic graphical model where observed variables correspond to the continuous (possibly high-dimensio...

Calculation of the Kullback-Leibler distance between point process models

We have developed a method for quantifying neural response changes in terms of the Kullback-Leibler distance between the intensity functions for each stimulus condition. We use empirical histogram estimates to characterize the intensity function of the neural response. A critical factor in determining the histogram estimates is selection of binwidth. In this work we analytically derive the Kull...

Kullback-leibler Distances for Quantifying Clutter and Models

This paper examines metrics for measuring clutter eeectiveness on model-based automatic target recognition systems with FLIR sensors. The measure for clutter eeectiveness proposed is the diierence of two Kullback-Leibler distances between the idealized approximate probabilistic models without clutter and the real model containing clutter. We establish that occluding objects and clutter, when ma...