Overview
- Oriented at students, developers and practitioners in machine learning and data analysis
- Provides useful insights into the role of parameters
- Interesting to historians
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11100)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Table of contents (18 chapters)
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Front Matter
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Bridging Past and Future
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Front Matter
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Novel Developments
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Front Matter
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Keywords
- artificial intelligence
- classification accuracy
- cluster analysis
- clustering algorithms
- data mining
- kernel function
- learning algorithms
- machine learning
- mixture modeling
- models of learning
- multivariate statistics
- probability
- robotics
- semi-supervised learning
- supervised learning
- Support Vector Machines (SVM)
- algorithm analysis and problem complexity
About this book
The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th anniversary of Emmanuil Braverman's decease. The papers present an overview of some of Braverman's ideas and approaches.
The collection is divided in three parts. The first part bridges the past and the present and covers the concept of kernel function and its application to signal and image analysis as well as clustering. The second part presents a set of extensions of Braverman's work to issues of current interest both in theory and applications of machine learning. The third part includes short essaysby a friend, a student, and a colleague.
Editors and Affiliations
About the editors
Ilya Muchnik, Rutgers University, Piscataway, NJ, USA.
Bibliographic Information
Book Title: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State
Book Subtitle: International Conference Commemorating the 40th Anniversary of Emmanuil Braverman's Decease, Boston, MA, USA, April 28-30, 2017, Invited Talks
Editors: Lev Rozonoer, Boris Mirkin, Ilya Muchnik
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-319-99492-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Softcover ISBN: 978-3-319-99491-8Published: 24 August 2018
eBook ISBN: 978-3-319-99492-5Published: 30 August 2018
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XII, 353
Number of Illustrations: 65 b/w illustrations
Topics: Artificial Intelligence, Algorithm Analysis and Problem Complexity, Probability and Statistics in Computer Science, Data Mining and Knowledge Discovery