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

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Springer, 2018 - Artificial intelligence - 353 pages
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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.
 

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Contents

Potential Functions for Signals and Symbolic Sequences
3
A Unified Framework for Clustering
32
Compactness Hypothesis Potential Functions and Rectifying Linear Space in Machine Learning
52
Conformal Predictive Distributions with Kernels
103
On the Concept of Compositional Complexity
122
On the Choice of a Kernel Function in Symmetric Spaces
128
Causality Modeling and Statistical Generative Mechanisms
148
Novel Developments
187
Geometrical Insights for Implicit Generative Modeling
229
Applications to Physics
269
An Overview
298
Personal and Beyond
329
A Man of Unlimited Capabilities in Memory of E M Braverman
331
Braverman and His Theory of Disequilibrium Economics
333
My Mentor and My Model
341
List of Bravermans Papers Published in the Avtomatika i telemekhanika Journal Moscow Russia and Translated to English as Automation and Remote...
349

OneClass Semisupervised Learning
188
Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients
201
On One Approach to Robot Motion Planning
213

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