Screen LibraryOptimization for data analysis /
"Optimization formulations and algorithms have long played a central role in data analysis and machine learning. Maximum likelihood concepts date to Gauss and Laplace in the late 1700s ; problems of this type drove developments in unconstrained optimization in the latter half of the 20th centur...
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| Main Authors: | |
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| Group Author: | |
| Published: |
Cambridge University Press,
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| Publisher Address: | Cambridge, United Kingdom : |
| Publication Dates: | 2022. |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| Summary: |
"Optimization formulations and algorithms have long played a central role in data analysis and machine learning. Maximum likelihood concepts date to Gauss and Laplace in the late 1700s ; problems of this type drove developments in unconstrained optimization in the latter half of the 20th century. Mangasarian's papers in the 1960s on pattern separation using linear programming made an explicit connection between machine learning and optimization in the early days of the former subject. During the 1990s, optimization techniques (especially quadratic programming and duality) were key to the development of support vector machines and kernel learning. The period 1997-2010 saw many synergies emerge between regularized / sparse optimization, variable selection, and compressed sensing. In the current era of deep learning, two optimization techniques-stochastic gradient and automatic differentiation (a.k.a. back-propagation)-are essential"-- |
| Carrier Form: | x, 227 pages : illustrations ; 24 cm |
| Bibliography: | Includes bibliographical references (pages 216-222) and index. |
| ISBN: |
9781316518984 1316518981 |
| Index Number: | QA76 |
| CLC: |
O224 TP311.13 |
| Call Number: | TP311.13/W953 |