In pattern classification tasks, errors are introduced because of differences betweenthe true model and the one obtained via model estimation. Using likelihood-ratio based classification, it is possible to correct for this discrepancy by finding class-pair specific terms to adjust the likelihood ratio directly, and that can make class-pair preference relationships intransitive. Inthis work, we introduce new methodology that makes necessary corrections to the likelihood ratio, specifically those that are necessary toachieve perfect classification (but not perfect likelihood-ratio correction which can be overkill). The new corrections, while weaker than previously reported such adjustments, are analytically challenging since they involve discontinuous functions, therefore requiring several approximations. We test a number of these new schemes on an isolatedword speechrecognition task as well as on the UCI machine learning data sets. Results show that by using the bias terms calculated in this new way, classification accuracy can substantially improve over both the baseline and over our previous results.

In this paper we introduce another prior over the dynamics of a(t). We assume that fluctuations in a(t) have a 1 / f spectrum, as observed ubiquitously in nature.

Recent developments in grid-based and point-based approximation algorithms forPOMDPs have greatly improved the tractability of POMDP planning. These approaches operate on sets of belief points by individually learninga value function for each point. In reality, belief points exist in a highly-structured metric simplex, but current POMDP algorithms donot exploit this property. This paper presents a new metric-tree algorithm which can be used in the context of POMDP planning to sort belief points spatially, and then perform fast value function updates over groups of points. We present results showing that this approach can reduce computationin point-based POMDP algorithms for a wide range of problems.

Most often, such recordings take the form of streams of discrete symbols ordered in time.

We present a Bayesian approach to color constancy which utilizes a non-Gaussian probabilistic model of the image formation process.

When we model a higher order functions, such as learning and memory, we face a difficulty of comparing neural activities with hidden variables that depend on the history of sensory and motor signals and the dynamics ofthe network. Here, we propose novel method for estimating hidden variables of a learning agent, such as connection weights from sequences of observable variables. Bayesian estimation is a method to estimate the posterior probability of hidden variables from observable data sequence using a dynamic model of hidden and observable variables.

We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional probabilities. This framework encompasses a wide variety of probability distributions, including both discrete and continuous random variables. We utilize ideas from large deviation analysis and the delta method to devise and evaluate a class of approximate inference algorithms forlayered Bayesian networks that have superior asymptotic error bounds and very fast computation time.

In models that define probabilities via energies, maximum likelihood learning typically involves using Markov Chain Monte Carlo to sample from the model's distribution. If the Markov chain is started at the data distribution, learning often works well even if the chain is only run for a few time steps [3]. But if the data distribution contains modes separated by regions of very low density, brief MCMC will not ensure that different modes have the correct relative energies because it cannot move particles from one mode to another. We show how to improve brief MCMC by allowing long-range moves that are suggested by the data distribution. If the model is approximately correct, these long-range moves have a reasonable acceptance rate.

Existing source location and recovery algorithms used in magnetoencephalographic imaginggenerally assume that the source activity at different brain locations is independent or that the correlation structure is known.

This paper describes a system that can annotate a video sequence with: a description of the appearance of each actor; when the actor is in view; and a representation of the actor's activity while in view. The system does not require a fixed background, and is automatic. The system works by (1) tracking people in 2D and then, using an annotated motion capture dataset, (2) synthesizing an annotated 3D motion sequence matching the 2D tracks. The 3D motion capture data is manually annotated off-line using a class structure that describes everyday motions and allows motion annotationsto be composed -- one may jump while running, for example. Descriptions computed from video of real motions show that the method is accurate.