Authors:
- Representation learning for cutting-edge machine learning – the benefit is a unifying approach to data fusion and transformation into compact tabular format used in standard learners and modern deep neural classifiers
- Coverage of tables, relations, texts, networks and ontologies – the benefit is a unified approach to handling heterogeneous data, enabling data scientists to step out of their isolated machine learning silos used in their routine practice
- Open science approach with hands-on examples – the benefit is the methodology and code reuse, as well as replicability with demo use cases
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (7 chapters)
-
Front Matter
-
Back Matter
About this book
Authors and Affiliations
-
Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia; School of Engineering and Management, University of Nova Gorica, Vipava, Slovenia
Nada Lavrač
-
Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
Vid Podpečan
-
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Marko Robnik-Šikonja
About the authors
Vid Podpečan, PhD, is a research associate at the Department of Knowledge Technologies at the Jožef Stefan Institute. He obtained his BSc in computer science from the University of Ljubljana in 2007, and his PhD from the Jožef Stefan International Postgraduate School in 2013. His research interests include machine learning, computational systems biology, text mining and natural language processing, and robotics. He co-authored a scientific monograph and published the results of his research in more than 50 scientific publications. He is also actively involved in promoting STEAM with a focus on robotics, programming, and art for which he received an award by the Slovene Science Foundation.
Prof Marko Robnik-Sikonja is Professor of Computer Science and Informatics at University of Ljubljana, Faculty of Computer and Information Science. His research interests span machine learning, data mining, natural languageprocessing, network analytics, and application of data science techniques. His most notable scientific results are from the areas of feature evaluation, ensemble learning, explainable artificial intelligence, data generation, and natural language analytics. He is (co)author of over 150 scientific publications that were cited more than 5,000 times, and three open-source R data mining packages. He participates in several national and international projects, regularly serves as programme committees member of top artificial intelligence and machine learning conferences, and is an editorial board member of seven international journals.
Bibliographic Information
Book Title: Representation Learning
Book Subtitle: Propositionalization and Embeddings
Authors: Nada Lavrač, Vid Podpečan, Marko Robnik-Šikonja
DOI: https://doi.org/10.1007/978-3-030-68817-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-68816-5Published: 11 July 2021
Softcover ISBN: 978-3-030-68819-6Published: 11 July 2022
eBook ISBN: 978-3-030-68817-2Published: 10 July 2021
Edition Number: 1
Number of Pages: XVI, 163
Number of Illustrations: 8 b/w illustrations, 38 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Data Structures, Numerical Analysis