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
Springer, Aug 30, 2018 - Computers - 353 pages
This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing 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 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 essays by a friend, a student, and a colleague.
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A Unified Framework for Clustering
Compactness Hypothesis Potential Functions and Rectifying Linear Space in Machine Learning
Conformal Predictive Distributions with Kernels
On the Concept of Compositional Complexity
On the Choice of a Kernel Function in Symmetric Spaces
Causality Modeling and Statistical Generative Mechanisms
Geometrical Insights for Implicit Generative Modeling
Applications to Physics
Personal and Beyond
A Man of Unlimited Capabilities in Memory of E M Braverman
Braverman and His Theory of Disequilibrium Economics
My Mentor and My Model
List of Bravermans Papers Published in the Avtomatika i telemekhanika Journal Moscow Russia and Translated to English as Automation and Remote...
OneClass Semisupervised Learning
Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients
On One Approach to Robot Motion Planning
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