This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.

Fisher linear discriminant analysis (LDA) can be sensitive to the problem data.

A dynamic texture is a video model that treats a video as a sample from a spatiotemporal stochastic process, specifically a linear dynamical system. Oneproblem associated with the dynamic texture is that it cannot model video where there are multiple regions of distinct motion. In this work, we introduce the layered dynamic texture model, which addresses this problem. We also introduce a variant of the model, and present the EM algorithm for learning each of the models. Finally, we demonstrate the efficacy of the proposed model for the tasks of segmentation and synthesis ofvideo.

The stochastic meta--descent (SMD) gain adaptation algorithm [3, 4] can considerably accelerate the convergence of stochastic gradient descent.

We argue that when objects are characterized by many attributes, clustering themon the basis of a relatively small random subset of these attributes can capture information on the unobserved attributes as well. Moreover, we show that under mild technical conditions, clustering the objects on the basis of such a random subset performs almost as well as clustering with the full attribute set. We prove a finite sample generalization theoremsfor this novel learning scheme that extends analogous results from the supervised learning setting. The scheme is demonstrated for collaborative filtering of users with movies rating as attributes.

We describe a hierarchy of motif-based kernels for multiple alignments of biological sequences, particularly suitable to process regulatory regions ofgenes. The kernels incorporate progressively more information, with the most complex kernel accounting for a multiple alignment of orthologous regions, the phylogenetic tree relating the species, and the prior knowledge that relevant sequence patterns occur in conserved motif blocks.These kernels can be used in the presence of a library of known transcription factor binding sites, or de novo by iterating over all k-mers of a given length. In the latter mode, a discriminative classifier builtfrom such a kernel not only recognizes a given class of promoter regions,but as a side effect simultaneously identifies a collection of relevant, discriminative sequence motifs. We demonstrate the utility of the motif-based multiple alignment kernels by using a collection ofaligned promoter regions from five yeast species to recognize classes of cell-cycle regulated genes.

Let x E RIXI be a Visible pattern7 and y E {3/1, . . .

Although nonparametric tests have already been proposed for that purpose, statisticalsignificance tests for nonstandard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically verifying how these tests compare with more classical tests, on various conditions. More precisely, using a very large dataset to estimate the whole "population", we analyzed the behavior ofseveral statistical test, varying the class unbalance, the compared models, the performance measure, and the sample size. The main result isthat providing big enough evaluation sets nonparametric tests are relatively reliable in all conditions.

Convexity has recently received a lot of attention in the machine learning community, and the lack of convexity has been seen as a major disadvantage ofmany learning algorithms, such as multi-layer artificial neural networks. We show that training multi-layer neural networks in which the number of hidden units is learned can be viewed as a convex optimization problem. This problem involves an infinite number of variables, but can be solved by incrementally inserting a hidden unit at a time, each time finding a linear classifier that minimizes a weighted sum of errors.

We present an efficient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the function isabove and below a given threshold. We develop experiment selection methodsbased on entropy, misclassification rate, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are used to determine simultaneously valid 1 α confidence intervals for seven cosmological parameters. Experimentation showsthat the algorithm reduces the computation necessary for the parameter estimation problem by an order of magnitude.