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Modern Methodology and Applications in Spatial-Temporal Modeling

Part of the SpringerBriefs in Statistics Book Series

Editors: Peters, Gareth William; Matsui, Tomoko

DOI: 10.1007/978-4-431-55339-7

ISBN: 978-4-431-55338-0

Publisher: Springer Japan - Springer Journals

Jan 9, 2016

Available

Nonparametric Bayesian Inference with Kernel Mean Embedding

Fukumizu, Kenji

Pages: 1 –24

[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...
Available

How to Utilize Sensor Network Data to Efficiently Perform Model Calibration and Spatial Field Reconstruction

Peters, Gareth W.; Nevat, Ido; Matsui, Tomoko

Pages: 25 –62

[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...
Available

Speech and Music Emotion Recognition Using Gaussian Processes

Markov, Konstantin; Matsui, Tomoko

Pages: 63 –85

[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...
Available

Topic Modeling for Speech and Language Processing

Chien, Jen-Tzung

Pages: 87 –111

[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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Modern Methodology and Applications in Spatial-Temporal Modeling

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