Hidden Markov Models and Applications
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Beschreibung
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 HMMsin 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.
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
Autor*in
Nizar Bouguila
Themen in »Hidden Markov Models and Applications«
Hidden Markov models machine learning, inference Bayesian learning infinite models nonparametric Bayesian supervised learning semi-supervised learning unsupervised learning