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.

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.

Suppose there exists an unknown real-valued property of the feature space, p(¢), that maps from the feature space, ¢ ERn, to R. The property function and a positive set A c

We analyze the bit error probability of multiuser demodulators for directsequence binaryphase-shift-keying (DSIBPSK) CDMA channel with additive gaussian noise. The problem of multiuser demodulation is cast into the finite-temperature decoding problem, and replica analysis is applied toevaluate the performance of the resulting MPM (Marginal Posterior Mode)demodulators, which include the optimal demodulator and the MAP demodulator as special cases. An approximate implementation ofdemodulators is proposed using analog-valued Hopfield model as a naive mean-field approximation to the MPM demodulators, and its performance is also evaluated by the replica analysis. Results of the performance evaluationshows effectiveness of the optimal demodulator and the mean-field demodulator compared with the conventional one, especially inthe cases of small information bit rate and low noise level. 1 Introduction The CDMA (Code-Division-Multiple-Access) technique [1] is important as a fundamental technology of digital communications systems, such as cellular phones. The important applications includerealization of spread-spectrum multipoint-to-point communications systems, in which multiple users share the same communication channel.

In this paper, we derive a second order mean field theory for directed graphical probability models. By using an information theoretic argument itis shown how this can be done in the absense of a partition function. This method is a direct generalisation of the well-known TAP approximation for Boltzmann Machines. In a numerical example, it is shown that the method greatly improves the first order mean field approximation. Fora restricted class of graphical models, so-called single overlap graphs, the second order method has comparable complexity to the first order method. For sigmoid belief networks, the method is shown to be particularly fast and effective.

This allows for the representation and exact solution of a wide range of problems in which transitions or rewards vary over time. We examine problems based on route planning with public transportation andtelescope observation scheduling. 1 Introduction Imagine trying to plan a route from home to work that minimizes expected time. One approach is to use a tool such as "Mapquest", which annotates maps with information about estimated driving time, then applies a standard graph-search algorithm to produce a shortest route.

Experimental data have shown that synapses are heterogeneous: different synapses respond with different sequences of amplitudes of postsynaptic responses to the same spike train. Neither the role of synaptic dynamics itself nor the role of the heterogeneity of synaptic dynamics for computations inneural circuits is well understood.

We describe a neurally-inspired, unsupervised learning algorithm that builds a nonlinear generative model for pairs of face images from the same individual. Individuals are then recognized by finding the highest relative probability pair among all pairs that consist of a test image and an image whose identity is known. Our method compares favorably with other methods in the literature. The generative model consists of a single layer of rate-coded, nonlinear feature detectors and it has the property that, given a data vector, the true posterior probability distribution over the feature detector activities can be inferred rapidly without iteration or approximation. The weights of the feature detectors are learned by comparing thecorrelations of pixel intensities and feature activations in two phases: When the network is observing real data and when it is observing reconstructions of real data generated from the feature activations.

A serious problem in learning probabilistic models is the presence of hidden variables.These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies amongthe latter. In recent years, much attention has been devoted to the development of algorithms for learning parameters, and in some cases structure, in the presence of hidden variables. In this paper, weaddress the related problem of detecting hidden variables that interact with the observed variables. This problem is of interest both for improving our understanding of the domain and as a preliminary step that guides the learning procedure towards promising models.

Classificationis achieved by a linear or nonlinear separating surface in the input space of the dataset. In this work we propose a very fast simple algorithm, based on an active set strategy for solving quadratic programs with bounds [18]. The algorithm is capable of accurately solving problems with millions of points and requires nothing more complicated than a commonly available linear equation solver [17, 1, 6] for a typically small (100) dimensional input space of the problem. Key to our approach are the following two changes to the standard linear SVM: 1. Maximize the margin (distance) between the parallel separating planes with respect to both orientation (w) as well as location relative to the origin b).