Recent Trends in Learning From Data

Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019)
1. Auflage 2020
ISBN: 978-3-030-43885-2
Verlag: Springer International Publishing

Buch, Englisch, Band 896, 221 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 359 g

Reihe: Studies in Computational Intelligence

This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.

Zielgruppe

Research

Autoren/Hrsg.

Oneto, Luca

Anguita, Davide

Navarin, Nicolò

Sperduti, Alessandro

Fachgebiete

• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie

Inhaltsverzeichnis

Introduction: Recent Trends in Learning From Data.- Learned data structures.- Deep Randomized Neural Networks.- Tensor Decompositions and Practical Applications.- Deep learning for graphs.- Limitations of Shallow Networks.- Fairness in Machine Learning.- Online Continual Learning on Sequences.