We propose probabilistic generative models, called parametric mixture models(PMMs), for multiclass, multi-labeled text categorization problem.Conventionally, the binary classification approach has been employed, in which whether or not text belongs to a category isjudged by the binary classifier for every category. In contrast, our approach can simultaneously detect multiple categories of text using PMMs. We derive efficient learning and prediction algorithms forPMMs. We also empirically show that our method could significantly outperform the conventional binary methods when applied tomulti-labeled text categorization using real World Wide Web pages.

An essential step in understanding the function of sensory nervous systems isto characterize as accurately as possible the stimulus-response function (SRF) of the neurons that relay and process sensory information. Oneincreasingly common experimental approach is to present a rapidly varying complex stimulus to the animal while recording the responses ofone or more neurons, and then to directly estimate a functional transformation of the input that accounts for the neuronal firing. The estimation techniques usually employed, such as Wiener filtering or other correlation-based estimation of the Wiener or Volterra kernels, are equivalent to maximum likelihood estimation in a Gaussian-output-noise regression model. We explore the use of Bayesian evidence-optimization techniques to condition these estimates. We show that by learning hyperparameters thatcontrol the smoothness and sparsity of the transfer function it is possible to improve dramatically the quality of SRF estimates, as measured by their success in predicting responses to novel input.

Several authors have suggested viewing boosting as a gradient descent search for a good fit in function space. We apply gradient-based boosting methodology to the unsupervised learning problem of density estimation. We show convergence properties of the algorithm and prove that a strength of weak learnability property appliesto this problem as well. We illustrate the potential of this approach through experiments with boosting Bayesian networks to learn density models.

We consider two ubiquitous processes:an industrial dryer and a level tank. For these applications, wecompared three particle filtering variants: standard particle filtering, Rao-Blackwellised particle filtering and a version of Rao-Blackwellised particle filtering that does one-step look-ahead to select good sampling regions. We show that the overhead of the extra processing perparticle of the more sophisticated methods is more than compensated bythe decrease in error and variance.

We examine the problem of generating state-space compressions of POMDPs in a way that minimally impacts decision quality. We analyze the impact of compressions ondecision quality, observing that compressions that allow accurate policy evaluation (prediction of expected future reward) will not affect decision quality. Wederive a set of sufficient conditions that ensure accurate prediction in this respect, illustrate interesting mathematical properties these confer on lossless linear compressions,and use these to derive an iterative procedure for finding good linear lossy compressions. We also elaborate on how structured representations of a POMDP can be used to find such compressions.

The direct neural control of external devices such as computer displays or prosthetic limbs requires the accurate decoding of neural activity representing continuousmovement. We develop a real-time control system using the spiking activity of approximately 40 neurons recorded with an electrode array implanted in the arm area of primary motor cortex. In contrast to previous work, we develop a control-theoretic approach that explicitly models the motion of the hand and the probabilistic relationship betweenthis motion and the mean firing rates of the cells in 70§ bins. We focus on a realistic cursor control task in which the subject mustmove a cursor to "hit" randomly placed targets on a computer monitor. Encoding and decoding of the neural data is achieved with a Kalman filter which has a number of advantages over previous linear filtering techniques. In particular, the Kalman filter reconstructions of hand trajectories in off-line experiments are more accurate than previously reportedresults and the model provides insights into the nature of the neural coding of movement.

We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation ofprior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method. We also attempt more challenging multidimensionalintegrals involved in computing marginal likelihoods ofstatistical models (a.k.a.

The problem of extracting the relevant aspects of data, in face of multiple conflicting structures, is inherent to modeling of complex data. Extracting structurein one random variable that is relevant for another variable has been principally addressed recently via the information bottleneck method [15]. However, such auxiliary variables often contain more information thanis actually required due to structures that are irrelevant for the task. In many other cases it is in fact easier to specify what is irrelevant than what is, for the task at hand. Identifying the relevant structures, however, can thus be considerably improved by also minimizing theinformation about another, irrelevant, variable. In this paper we give a general formulation of this problem and derive its formal, as well as algorithmic, solution. Its operation is demonstrated in a synthetic example and in two real world problems in the context of text categorization andface images. While the original information bottleneck problem is related to rate distortion theory, with the distortion measure replaced by the relevant information, extracting relevant features while removing irrelevant ones is related to rate distortion with side information.

We present a new technique for achieving source separation when given only a single channel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of basis filters in time domain that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis filters.

Prior knowledge in the form of multiple polyhedral sets, each belonging toone of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resulting formulation leadsto a linear program that can be solved efficiently. Real world examples, from DNA sequencing and breast cancer prognosis, demonstrate the effectiveness of the proposed method. Numerical results show improvement in test set accuracy after the incorporation ofprior knowledge into ordinary, data-based linear support vector machine classifiers. One experiment also shows that a linear classifier,based solely on prior knowledge, far outperforms the direct application of prior knowledge rules to classify data.