Name: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of Emma (en Inglés)
SKU: 58637439
Price: 841.79 MXN
Availability: InStock
Author: Rozonoer, Lev ; Mirkin, Boris ; Muchnik, Ilya
ISBN: 9783319994918

portada Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of Emma (en Inglés)

Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of Emma (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Rozonoer, Lev ; Mirkin, Boris ; Muchnik, Ilya

Idioma

Inglés

N° páginas

353

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x1.9 cm

Peso

0.51 kg.

ISBN13

9783319994918

Categorías

Software de matemáticas y estadísticas
Algoritmos y estructuras de datos
Inteligencia artificial

Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of Emma (en Inglés)

Rozonoer, Lev ; Mirkin, Boris ; Muchnik, Ilya (Autor) · Springer · Tapa Blanda

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Reseña del libro "Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of Emma (en Inglés)"

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.