Hidden Markov models and applications / Nizar Bouguila, Wentao Fan, Manar Amayri, editors.

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Hidden Markov models and applications / Nizar Bouguila, Wentao Fan, Manar Amayri, editors.

9783030991425 (electronic bk.)
3030991423 (electronic bk.)
9783030991418
3030991415

Cham : Springer, [2022]

©2022

English

1 online resource (x, 298 pages) : illustrations (chiefly color).

10.1007/978-3-030-99142-5 doi

QA274.7

519.2/33

This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications. Includes new advances on finite and infinite Hidden Markov Models (HMMs) and their applications from different disciplines; Tackles recent challenges related to the deployment of HMMs in real-life applications (e.g., big data, multimodal data, etc.); Presents new applications of HMMs by considering advancements with respect to inference techniques and recent technological advancements.

Includes index.

References -- Using HMM to Model Neural Dynamics and Decode Useful Signals for Neuroprosthetic Control -- 1 Introduction -- 2 General Principles OF HMM -- 3 Neural Implementation of HMMs -- 4 HMMs of Parietal Cortex Activity During an Arm Movement Task -- 4.1 Functional Characterization of the Parietal Cortex Areas -- 4.2 Decoding of Task Epoch and Target Position -- 4.3 HMM: Parietal Cortex Functions and Information Decoding -- 5 Related Works -- 5.1 HMM Applications to Model Neurophysiological Data -- 5.2 Other Approaches to Model Neurophysiological Data -- 6 Conclusions -- References.

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Online resource; title from PDF title page (SpringerLink, viewed May 31, 2022).

Unsupervised and semi-supervised learning.

Print version: 9783030991418

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