We propose a novel probabilistic framework for semantic video indexing. Wedefine probabilistic multimedia objects (multijects) to map low-level media features to high-level semantic labels.

In this paper we develop the method of bounding the generalization error of a classifier in terms of its margin distribution which was introduced in the recent papers of Bartlett and Schapire, Freund, Bartlett and Lee. The theory of Gaussian and empirical processes allow us to prove the margin type inequalities for the most general functional classes, the complexity of the class being measured via the so called Gaussian complexity functions. Asa simple application of our results, we obtain the bounds of Schapire, Freund, Bartlett and Lee for the generalization error of boosting. Wealso substantially improve the results of Bartlett on bounding the generalization error of neural networks in terms of h -norms of the weights of neurons. Furthermore, under additional assumptions on the complexity of the class of hypotheses we provide some tighter bounds, which in the case of boosting improve the results of Schapire, Freund, Bartlett and Lee.

Ming-Hsuan Yang University of Illinois at Urbana-Champaign Urbana, IL 61801 USA mhyang avision.ai.uiuc.edu Abstract Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor)as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble RBF networks. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature. 1 Introduction In recent years, SVMs have been successfully applied to various tasks in computational face-processing.These include face detection [14], face pose discrimination [12] and face recognition [16]. Although facial sex classification has attracted much attention in the psychological literature [1, 4, 8, 15], relatively few computatinal learning methods have been proposed.

Igor V. Cadez Information and Computer Science University of California Irvine, CA 92697-3425, U.S.A. PadhraicSmyth Information and Computer Science University of California Irvine, CA 92697-3425, U.S.A. Abstract We investigate a general characteristic of the tradeoff in learning problems between goodness-of-fit and model complexity. Specifically wecharacterize a general class of learning problems where the goodness-of-fit function can be shown to be convex within firstorder asa function of model complexity. This general property of "diminishing returns" is illustrated on a number of real data sets and learning problems, including finite mixture modeling and multivariate linear regression. 1 Introduction, Motivation, and Related Work Assume we have a data set D Such learning tasks can typically be characterized by the existence of a model and a loss function. A fitted model of complexity k is a function of the data points D and depends on a specific set of fitted parameters B. The loss function (goodnessof-fit) isa functional of the model and maps each specific model to a scalar used to evaluate the model, e.g., likelihood for density estimation or sum-of-squares for regression. Figure 1 illustrates a typical empirical curve for loss function versus complexity, for mixtures of Markov models fitted to a large data set of 900,000 sequences.

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data using switching linear dynamic system (SLDS) models. We present results for human motion synthesis, classification,and visual tracking using learned SLDS models. Since exact inference in SLDS is intractable, we present three approximate inference algorithmsand compare their performance. In particular, a new variational inference algorithm is obtained by casting the SLDS model as a Dynamic Bayesian Network. Classification experiments show the superiority of SLDS over conventional HMM's for our problem domain.

Novelty detection involves modeling the normal behaviour of a system henceenabling detection of any divergence from normality. It has potential applications in many areas such as detection of machine damageor highlighting abnormal features in medical data. One approach is to build a hypothesis estimating the support of the normal data i.e. constructing a function which is positive in the region where the data is located and negative elsewhere. Recently kernel methods have been proposed for estimating the support of a distribution and they have performed well in practice - training involves solution of a quadratic programming problem. In this paper wepropose a simpler kernel method for estimating the support based on linear programming. The method is easy to implement and can learn large datasets rapidly. We demonstrate the method on medical and fault detection datasets.

Most models of spatial representations in the cortex assume cells with limited receptive fields that are defined in a particular egocentric frameof reference. However, cells outside of primary sensory cortex are either gain modulated by postural input or partially shifting. We show that solving classical spatial tasks, like sensory prediction,multi-sensory integration, sensory-motor transformation andmotor control requires more complicated intermediate representations that are not invariant in one frame of reference. We present an iterative basis function map that performs these spatial tasks optimally with gain modulated and partially shifting units, and tests it against neurophysiological and neuropsychological data. In order to perform an action directed toward an object, it is necessary to have a representation of its spatial location.

We introduce a novel algorithm, termed PPA (Performance Prediction Algorithm), that quantitatively measures the contributions of elements of a neural system to the tasks it performs. The algorithm identifies the neurons or areas which participate in a cognitive or behavioral task, given data about performance decrease in a small set of lesions. It also allows the accurate prediction of performances due to multi-element lesions. The effectiveness of the new algorithm is demonstrated in two models of recurrent neural networks with complex interactions among the elements. Thealgorithm is scalable and applicable to the analysis of large neural networks. Given the recent advances in reversible inactivation techniques, it has the potential to significantly contribute to the understanding ofthe organization of biological nervous systems, and to shed light on the long-lasting debate about local versus distributed computation inthe brain.

We implement this description for the Hodgkin Huxley model, identify the most relevant dimensions and find the nonlinearity. A two dimensional description already captures a significant fraction of the information that spikes carry about dynamic inputs.This description also shows that computation in the Hodgkin-Huxley model is more complex than a simple integrateand-fire orperceptron model. 1 Introduction Classical neural network models approximate neurons as devices that sum their inputs and generate a nonzero output if the sum exceeds a threshold. From our current state of knowledge in neurobiology it is easy to criticize these models as oversimplified: whereis the complex geometry of neurons, or the many different kinds of ion channel, each with its own intricate multistate kinetics? Indeed, progress at this more microscopic level of description has led us to the point where we can write (almost) exact models for the electrical dynamics of neurons, at least on short time scales. These nearly exact models are complicated by any measure, including tens if not hundreds of differential equations to describe the states of different channels in different spatial compartments of the cell. Faced with this detailed microscopic description, we need to answer a question which goes well beyond the biological context: given a continuous dynamical system, what does it compute? Our goal in this paper is to make this question about what a neuron computes somewhat moreprecise, and then to explore what we take to be the simplest example, namely the Hodgkin-Huxley model [1],[2] (and refs therein).

Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithmsfor NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence. The monotonic convergence of both algorithms can be proven using an auxiliary function analogousto that used for proving convergence of the Expectation Maximization algorithm. The algorithms can also be interpreted as diagonally rescaledgradient descent, where the rescaling factor is optimally chosen to ensure convergence.