On the other hand, we use ADABOOST's efficient learning mechanism to significantly improve supervised and semi-supervised

A graph-based prior is proposed for parametric semi-supervised Classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple Views of a given sample (e.g., multiple

We present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the latent distribution non-parametrically which enables us to model data generated by an underlying low dimensional, multimodaldistribution. In addition, we allow the components of latent variable models to be drawn from the exponential family which makes the method suitable for special data types, for example binary or count data. Simulations on real valued, binary and count data show favorable comparisonto other related schemes both in terms of separating different populations and generalization to unseen samples.

We propose a novel a framework for deriving approximations for intractable probabilisticmodels. This framework is based on a free energy (negative log marginal likelihood) and can be seen as a generalization of adaptive TAP [1, 2, 3] and expectation propagation (EP) [4, 5]. The free energy is constructed from two approximating distributions which encode different aspects of the intractable model such a single node constraints andcouplings and are by construction consistent on a chosen set of moments. We test the framework on a difficult benchmark problem with binary variables on fully connected graphs and 2D grid graphs. We find good performance using sets of moments which either specify factorized nodesor a spanning tree on the nodes (structured approximation). Surprisingly, the Bethe approximation gives very inferior results even on grids.

Dixon describes beats as follows: "much music has as its rhythmic basis a series of pulses, spaced approximately equally in time, relative to which the timing of all musical events can be described. This phenomenon is called the beat, and the individual pulses are also called beats"[1]. Given a piece of recorded music (an MP3 file, for example), we wish to produce a set of beats that correspond to the beats perceived by human listeners. The set of beats of a song can be characterised by the trajectories through time of thetempo and phase offset. Tempo is typically measured in beats per minute (BPM), and describes the frequency of beats.

The area under the ROC curve (AUC) has been advocated as an evaluation criterion for the bipartite ranking problem.

Device mismatch in VLSI degrades the accuracy of analog arithmetic circuits and lowers the learning performance of large-scale neural networks implementedin this technology. We show compact, low-power on-chip calibration techniques that compensate for device mismatch. Our techniques enable large-scale analog VLSI neural networks with learning performanceon the order of 10 bits. We demonstrate our techniques on a 64-synapse linear perceptron learning with the Least-Mean-Squares (LMS) algorithm, and fabricated in a 0.35µm CMOS process.

There has been substantial progress in the past decade in the development of object classifiers for images, for example of faces, humans and vehicles. Herewe address the problem of contaminations (e.g.

The role played by each panel, experts and consumers, is very Clear.

In this paper we propose a probabilistic model for online document clustering. Weuse nonparametric Dirichlet process prior to model the growing number of clusters, and use a prior of general English language model as the base distribution to handle the generation of novel clusters. Furthermore, cluster uncertainty is modeled with a Bayesian Dirichletmultinomial distribution.We use empirical Bayes method to estimate hyperparameters based on a historical dataset. Our probabilistic model is applied to the novelty detection task in Topic Detection and Tracking (TDT) and compared with existing approaches in the literature.