Screen LibraryBraverman 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 /
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 Learnin...
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| Corporate Authors: | |
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| Group Author: | ; ; ; |
| Published: |
Springer,
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| Publisher Address: | Cham, Switzerland : |
| Publication Dates: | [2018] |
| Literature type: | Book |
| Language: | English |
| Series: |
Lecture notes in artificial intelligence,
11100 LNCS sublibrary. SL 7, Artificial intelligence |
| Subjects: | |
| Summary: |
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 |
| Carrier Form: | xii, 351 pages : illustrations, forms ; 24 cm. |
| Bibliography: | Includes bibliographical references and author index. |
| ISBN: |
9783319994918 (paperback) : 3319994913 (paperback) |
| Index Number: | Q325 |
| CLC: | TP181-532 |
| Call Number: | TP181-532/C748/2017 |
| Contents: | Potential Functions for Signals and Symbolic Sequences -- Braverman's Spectrum and Matrix Diagonalization versus iK-Means: 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 -- One-Class Semi-Supervised Learning -- Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Ca |