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[Kernel methods have been successfully used in many machine learning problems with favorable performance in extracting nonlinear structure of high-dimensional data. Recently, nonparametric inference methods with positive definite kernels have been developed, employing the kernel mean expression...
[This chapter provides a tutorial overview of some modern applications of the statistical modeling that can be developed based upon spatial wireless sensor network data. We then develop a range of new results relating to two important problems that arise in spatial field reconstructions from...
[Gaussian Processes (GPs) are Bayesian nonparametric models that are becoming more and more popular for their superior capabilities to capture highly nonlinear data relationships in various tasks ranging from classical regression and classification to dimension reduction, novelty detection and...
[In this chapter, we present state-of-art machine learning approaches for speech and language processing with highlight on topic models for structural learning and temporal modeling from unlabeled sequential patterns. In general, speech and language processing involves extensive knowledge of...
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