Artículos relacionados a Black Box Optimization, Machine Learning, and No-Free...
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems: 170 (Springer Optimization and Its Applications) - Tapa blanda
Sinopsis
This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Panos M. Pardalos serves as distinguished professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. Professor Pardalos is also an affiliated faculty member of the computer and information science department, the Hellenic Studies Center, and the biomedical engineering program. Additionally, he serves as the director of the Center for Applied Optimization. Professor Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, ecommerce, data mining, biomedical applications, and massive computing. Panos Pardalos is a prolific author who lectures all over the world. He is the recipient of a multitude of fellowships and awards, the most recent of which is the Humboldt Research Award (2018).
Varvara A. Rasskazova is aResearcher and Senior Lecture in Moscow Aviation Institute. The area of scientific interests is discrete optimization and guaranteed approximation algorithms for solving transportation and assignment problems on Railway and Metallurgical Production. She is an active participant of regular international conferences on Operations Research and Mathematical Programming, and author of more than 40 publications.
Michael N. Vrahatis focuses on mathematics, natural computing and computational intelligence, global optimization, reliable computing and imprecise data, artificial neural networks and machine learning. He has participated in the organization of over 300 conferences serving at several positions, and participated in more than 240 conferences, congresses and advanced schools as active participant, speaker or keynote speaker. He has been a visiting research fellow in many different institutions including Cornell, MIT, CERN, and INRIA. He is a professor in the Department of Mathematics at the University of Patras since 2000. He is also serving as the director of the newly founded Institute of Artificial Intelligence of University of Patras. The corpus of his work consists of over 400 publications. According to Google Scholar his work has been cited more than 16000 times (h-index 56).
De la contraportada
This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.
"Sobre este título" puede pertenecer a otra edición de este libro.
Comprar nuevo
Ver este artículo
EUR 118,61
EUR 19,49 gastos de envío desde Alemania a España
Destinos, gastos y plazos de envío
Resultados de la búsqueda para Black Box Optimization, Machine Learning, and No-Free...
Imagen del vendedor
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Publicado por
Springer International Publishing, 2022
ISBN 10: 3030665178
ISBN 13: 9783030665173
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Beginners will achieve an overview of optimization methodsResearchers will gain access to to a useful reference on key topicsMathematical rigour and heuristics approaches equip the reader with different viewpoints on the same problem. Nº de ref. del artículo: 608128385
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9783030665173_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Publicado por
Springer International Publishing Jun 2022, 2022
ISBN 10: 3030665178
ISBN 13: 9783030665173
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions.The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem. 400 pp. Englisch. Nº de ref. del artículo: 9783030665173
Cantidad disponible: 2 disponibles
Imagen del vendedor
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Publicado por
Springer International Publishing, 2022
ISBN 10: 3030665178
ISBN 13: 9783030665173
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions.The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem. Nº de ref. del artículo: 9783030665173
Cantidad disponible: 1 disponibles
Imagen de archivo
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9783030665173
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Publicado por
Springer International Publishing, Springer Nature Switzerland Jun 2022, 2022
ISBN 10: 3030665178
ISBN 13: 9783030665173
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 400 pp. Englisch. Nº de ref. del artículo: 9783030665173
Cantidad disponible: 1 disponibles
Imagen de archivo
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26395062733
Cantidad disponible: 4 disponibles
Imagen de archivo
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Print on Demand. Nº de ref. del artículo: 402362898
Cantidad disponible: 4 disponibles
Imagen de archivo
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020025939
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (Springer Optimization and Its Applications)
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. PRINT ON DEMAND. Nº de ref. del artículo: 18395062727
Cantidad disponible: 4 disponibles