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

Beschreibung

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.

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 essaysby a friend, a student, and a colleague.


Oriented at students, developers and practitioners in machine learning and data analysis Provides useful insights into the role of parameters Interesting to historians

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Lev Rozonoer

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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

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