pardalos panos principe jose (42 résultats)

Etat : NEW.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : New.

Etat : New. In.

Etat : New.

Hardcover. Etat : New.

Etat : New. In.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This English version of Ruslan L. Stratonovich'sTheory of Information(1975)builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.

Etat : New.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This English version of Ruslan L. Stratonovich'sTheory of Information(1975)builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.

Gebunden. Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Neuware -This English version of Ruslan L. Stratonovich¿s Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 444 pp. Englisch.

Etat : New. pp. XXII, 419 33 illus., 4 illus. in color. 1 Edition NO-PA16APR2015-KAP.

Etat : New. Includes seminal contributions in genomics, global optimization in biomedicine, computational neuroscience, FMRI, brain dynamics, and epileptic seizure prediction. This volume is suitable for students of biomedical engineering and biocomputing, and to the scientists in various disciplines who need to learn more about biomedical computing. Editor(s): Pardalos, Panos M.; Principe, Jose C. Series: Biocomputing. Num Pages: 274 pages, biography. BIC Classification: MBG; MQW. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 17. Weight in Grams: 571. . 2002. 2002nd Edition. hardcover. . . . .

Etat : As New. Unread book in perfect condition.

Etat : New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

Hardback. Etat : New. 2020 ed. This English version of Ruslan L. Stratonovich's Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth.  The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.

Etat : New.

Etat : New.

Hardback. Etat : New. 2020 ed. This English version of Ruslan L. Stratonovich's Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth.  The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.

Paperback. Etat : new. Paperback. This English version of Ruslan L. Stratonovichs Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New. Includes seminal contributions in genomics, global optimization in biomedicine, computational neuroscience, FMRI, brain dynamics, and epileptic seizure prediction. This volume is suitable for students of biomedical engineering and biocomputing, and to the scientists in various disciplines who need to learn more about biomedical computing. Editor(s): Pardalos, Panos M.; Principe, Jose C. Series: Biocomputing. Num Pages: 274 pages, biography. BIC Classification: MBG; MQW. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 17. Weight in Grams: 571. . 2002. 2002nd Edition. hardcover. . . . . Books ship from the US and Ireland.

Paperback. Etat : Brand New. 441 pages. 9.25x6.10x1.05 inches. In Stock.