Buch, Englisch, 208 Seiten, Format (B × H): 241 mm x 160 mm, Gewicht: 498 g
Buch, Englisch, 208 Seiten, Format (B × H): 241 mm x 160 mm, Gewicht: 498 g
ISBN: 978-0-367-32178-9
Verlag: Taylor & Francis Ltd
An important factor in classifying stochastic time series is the structural risk associated with the architecture of classification methods. The book addresses and formulates structural risk, and the learning capacity defined for a classification method. These formulations and the mathematical derivations will help the researchers in understanding the methods and even express their methodologies in an objective mathematical way. The book has been designed as a self-learning textbook for the readers with different backgrounds and understanding levels of machine learning, including students, engineers, researchers, and scientists of this domain. The numerous informative illustrations presented by the book will lead the readers to a deep level of understanding about the deep learning methods for time series analysis.
Zielgruppe
Academic
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
- Naturwissenschaften Biowissenschaften Biowissenschaften
- Mathematik | Informatik Mathematik Stochastik
Weitere Infos & Material
PREFACE. I-FUNDAMENTALS OF LEARNING. Introduction to Learning. Learning Theory. Pre-processing and Visualisation. II ESSENTIALS OF TIME SERIES ANALYSIS. Basics of Time Series. Multi-Layer Perceptron (MLP) Neural Networks for Time Series Classification. Dynamic Models for Sequential Data Analysis. III DEEP LEARNING APPROACHES TO TIME SERIES CLASSIFICATION. Clustering for Learning at Deep Level. Deep Time Growing Neural Network. Deep Learning of Cyclic Time Series. Hybrid Method for Cyclic Time Series. Recurrent Neural Networks (RNN). Convolutional Neural Networks. Bibliography.