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1. |
Record Nr. |
UNINA9910711919703321 |
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Titolo |
Social and economic implications of cancer in the United States : based on a presentation to the Expert Committee on Cancer Statistics of the World Health Organization and International Agency for Research on Cancer at Madrid, Spain, June 20 to 26, 1978 / / Dorothy P. Rice, and Thomas A. Hodgson |
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Pubbl/distr/stampa |
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Hyattsville, Md. : , : U.S. Department of Health and Human Services, Public Health Service, Office of Health Research, Statistics, and Technology, National Center for Health Statistics, , 1981 |
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Descrizione fisica |
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1 online resource (iv, 43 pages) : illustrations |
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Collana |
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Vital and health statistics. Series 3, Analytical studies ; ; number 20 |
DHHS publication ; ; no. (PHS) 81-1404 |
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Soggetti |
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Cancer - Economic aspects - United States |
Cancer - Social aspects - United States |
Cancer - Mortality - United States |
Cancer |
Cancer - Economic aspects |
Conference papers and proceedings. |
Statistics. |
United States |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references (pages 18-20). |
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2. |
Record Nr. |
UNINA9910409667103321 |
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Autore |
Lin Zhouchen |
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Titolo |
Accelerated optimization for machine learning : first-order algorithms / / Zhouchen Lin, Huan Li, Cong Fang |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2020] |
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©2020 |
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ISBN |
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981-15-2910-8 |
9789811529108 |
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Descrizione fisica |
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1 online resource (286 pages) |
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Disciplina |
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Soggetti |
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Machine learning - Mathematics |
Mathematical optimization |
Computer science - Mathematics |
Machine Learning |
Optimization |
Math Applications in Computer Science |
Computational Mathematics and Numerical Analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Chapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions. |
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Sommario/riassunto |
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This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for |
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machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time. |
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