Braverman Readings in Machine Learning. Key Ideas from Inception to Current State

International Conference Commemorating the 40th Anniversary of Emmanuil Braverman's Decease, Boston, MA, USA, April 28-30, 2017, Invited Talks

  • Lev Rozonoer
  • Boris Mirkin
  • Ilya Muchnik

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11100)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11100)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Bridging Past and Future

    1. Front Matter
      Pages 1-1
    2. Valentina Sulimova, Vadim Mottl
      Pages 3-31
    3. Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman
      Pages 103-121
    4. Lev I. Rozonoer
      Pages 122-127
    5. M. A. Aizerman, E. M. Braverman, Lev I. Rozonoer
      Pages 128-147
  3. Novel Developments

    1. Front Matter
      Pages 187-187
    2. Evgeny Bauman, Konstantin Bauman
      Pages 189-200
    3. Nicolas Borisov, Victor Tkachev, Anton Buzdin, Ilya Muchnik
      Pages 201-212
    4. Vladimir Lumelsky
      Pages 213-228
    5. Leon Bottou, Martin Arjovsky, David Lopez-Paz, Maxime Oquab
      Pages 229-268
    6. Peter Sadowski, Pierre Baldi
      Pages 269-297
    7. Forest Agostinelli, Guillaume Hocquet, Sameer Singh, Pierre Baldi
      Pages 298-328
  4. Personal and Beyond

  5. Back Matter
    Pages 353-353

About this book


This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing the machine learning theory. 

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. Its main contents relate to 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 essays by a friend, a student, and a colleague.


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)

Editors and affiliations

  1. 1.West NewtonUSA
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Rutgers UniversityPiscatawayUSA

Bibliographic information