Jerome h friedman (15 resultados)

Classification and Regression Trees (The Wadsworth Statistics/Probability Series)
Leo Breiman; Jerome H. Friedman; Richard A. Olshen; Charles J. Stone
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Classification and Regression Trees (The Wadsworth Statistics/Probability Series)
Leo Breiman; Jerome H. Friedman; Richard A. Olshen; Charles J. Stone
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Classification and Regression Trees (The Wadsworth Statistics/Pro
Leo Breiman; Jerome H. Friedman; Richard A. Olshen; Charles J. Stone
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CLASSIFICATION AND REGRESSION TREES.
Breiman, Leo, Jerome H. Friedman, Richard A. Olshen and Charles J. Stone.
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Soft Cover. Condición: Fine. 358 pages. "The tree methodology, discussed in this book is a child of the computer age. Unlike many other statistical procedures which were moved from pencil and paper to calculators, and then to computers, this use of trees was unthinkable before computers." FINE SOFTCOVER Size: 8vo - over 7¾" - 9¾…" tall.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to gether over 100 partici…pants (including 19 invited lecturers) from 20 countries. The invited lecturers whose contributions appear in this volume are: L. Almeida (INESC, Portugal), G. Carpenter (Boston, USA), V. Cherkassky (Minnesota, USA), F. Fogelman Soulie (LRI, France), W. Freeman (Berkeley, USA), J. Friedman (Stanford, USA), F. Girosi (MIT, USA and IRST, Italy), S. Grossberg (Boston, USA), T. Hastie (AT&T, USA), J. Kittler (Surrey, UK), R. Lippmann (MIT Lincoln Lab, USA), J. Moody (OGI, USA), G. Palm (U1m, Germany), B. Ripley (Oxford, UK), R. Tibshirani (Toronto, Canada), H. Wechsler (GMU, USA), C. Wellekens (Eurecom, France) and H. White (San Diego, USA). The ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (1) Unified framework for the study of Predictive Learning in Statistics and Artificial Neural Networks (ANNs); (2) Differences and similarities between statistical and ANN methods for non parametric estimation from examples (learning); (3) Fundamental connections between artificial learning systems and biological learning systems.

From Statistics to Neural Networks: Theory and Pattern Recognition Applications (Nato Asi Series (Closed) / Nato Asi Subseries F: (Closed))
Cherkassky, Vladimir (Editor) / Friedman, Jerome H. (Editor) / Wechsler, Harry (Editor)
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Paperback. Condición: Brand New. reprint edition. 406 pages. 9.00x6.10x1.00 inches. In Stock.

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Taschenbuch. Condición: Neu. From Statistics to Neural Networks | Theory and Pattern Recognition Applications | Vladimir Cherkassky (u. a.) | Taschenbuch | NATO ASI Subseries F: | xii | Englisch | 2011 | J.B. Metzler | EAN 9783642791215 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelber…g, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Condición: Sehr gut. Zustand: Sehr gut | Seiten: 406 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

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Condición: Sehr gut. Zustand: Sehr gut | Seiten: 406 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

High-Energy Inelastic e-p Scattering at 6(deg) and 10(deg) (Bloom et al., pp. 930-934) WITH Observed Behavior of Highly Inelastic Electron-Proton Scattering (Friedman et al., pp. 935-939) in Physical Review Letters 23, 1969
Bloom, E.D.; Coward, D.H.; DeStaebler, H; Drees, J.; Miller, G.; Mo, L. W.; Taylor, R. E. [Richard] AND Breidenback, M.; Friedman, J. [Jerome], L.; Hartmann, G. C.; Kendall, H.W. [Henry]
Editorial: The American Physical Society, New York, 1969
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Librería: Atticus Rare Books, West Branch, IA, Estados Unidos de AmericaAtticus Rare Books
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Añadir al carrito1st Edition. FULL VOLUME 1969 FIRST EDITION OF TWO PAPERS PRESENTING THE FIRST EMPIRICAL EVIDENCE OF THE EXISTENCE OF QUARKS WITHIN THE ATOMIC NUCLEUS. Quarks had been predicted in 1964 by Murray Gell-Mann but until these experiments, no one had produced convincing experimental evidence for the existence of quarks inside the pro…ton or neutron ("Friedman, Kendall and Taylor Win Nobel Prize for First Quark Evidence", Physics Today, Vol. 44, 1, pp. 17). In demonstrating that quarks are real particles, the lead scientists on these two papers, Jerome Friedman, Henry Kendall, and Richard Taylor confirmed Murray Gell-Mann's hypothesis of their existence and for their discovery, the trio were awarded the 1990 Nobel Prize in Physics. Taylor passed away in early 2018 a further note about his import appears below. The first paper, "High-Energy Inelastic e-p Scattering at 6(deg) and 10(deg)" "describes the experiment that identified the point-like centers within protons that were later identified as quarks" (The History of Science: Wenner Collection). The second, "Observed Behavior of Highly Inelastic Electron-Proton Scattering" "explains the significance of the experiment in terms of theory" (ibid). Gell-Mann's seminal 1964 work positing the 'idea' of quarks was based on the assumption that strongly interacting particles he classified (called hadrons) were all "built up from more elementary constituents" that he famously called 'quarks' (Levinovitz, The Nobel Prize, 44). Few people, however, "believed that quarks were real particles. Despite many searches in accelerators and in cosmic rays, no one had found an isolated quark. As Gell-Mann himself said: 'Such particles [quarks] presumably are not real but we may use them in our field theory anyway'" (Alan Lightman, The Discoveries, pp. 457). In the late 1960s while at the new two-mile electron linear accelerator, the Stanford Linear Accelerator Center (SLAC), Friedman, Kendall, and Taylor proved the existence of quarks by using "high-energy electrons from the then new accelerator, and [showed] that they bounced back in an unexpected way from the protons and neutrons in a gas target" (Quinn, The Mystery of the Missing Antimatter, 97). High-energy collisions - such as those enabled by SLAC - "disrupt the target, and the ensuing scattering is called inelastic. When high-energy electrons are used for this purpose, their wavelengths are small enough to probe within protons and neutrons inside the nucleus-that is, deep within the nucleus (the 'deep' within 'deep inelastic scattering'). In 1969, American theoretical physicist James Bjorken (born 1934) used a form of mathematics called current algebra to predict behavior in deep inelastic scattering (now called "Bjorken scaling") and proposed an experiment to test the theory of hadrons being comprised of smaller, point-like particles" (Wenner). Friedman, Kendall, and Taylor then "conducted the Bjorken scaling experiment using liquid hydrogen as the target the Bjorken scaling experiment using liquid hydrogen as the target" (ibid). "Friedman's group was startled. to observe that the scattering pattern suggested not that the positive charge of the proton was uniformly spread out, but rather that charges were confined to point-like centers within the protons. As the Nobel Prize Committee's presentation speech makes clear, Friedman, Kendall, and Taylor had not anticipated anything fundamentally new: "similar experiments, albeit at lower energies, had found that the proton behaved like a soft gelatinous sphere with many excited states, similar to those of atoms and nuclei. Nevertheless, the Laureates decided to go one step further and study the proton under extreme conditions. They looked for the electron undergoing a large deflection, and where the proton, rather than keeping its identity, seized a lot of the collision energy and broke up into a shower of new particles. This so called "deep inelastic scattering" had generally been considered to be too rare to be w.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to gether…over 100 participants (including 19 invited lecturers) from 20 countries. The invited lecturers whose contributions appear in this volume are: L. Almeida (INESC, Portugal), G. Carpenter (Boston, USA), V. Cherkassky (Minnesota, USA), F. Fogelman Soulie (LRI, France), W. Freeman (Berkeley, USA), J. Friedman (Stanford, USA), F. Girosi (MIT, USA and IRST, Italy), S. Grossberg (Boston, USA), T. Hastie (AT&T, USA), J. Kittler (Surrey, UK), R. Lippmann (MIT Lincoln Lab, USA), J. Moody (OGI, USA), G. Palm (U1m, Germany), B. Ripley (Oxford, UK), R. Tibshirani (Toronto, Canada), H. Wechsler (GMU, USA), C. Wellekens (Eurecom, France) and H. White (San Diego, USA). The ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (1) Unified framework for the study of Predictive Learning in Statistics and Artificial Neural Networks (ANNs); (2) Differences and similarities between statistical and ANN methods for non parametric estimation from examples (learning); (3) Fundamental connections between artificial learning systems and biological learning systems. 416 pp. Englisch.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Proceedings of the NATO Advances Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications, held in Les Arcs, Bourg Saint Maurice, France, June 21 - July 2, 1993The NATO Advanced…Study Institute From Statistics to N.

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This volume provides a unified approach to the study of predictive learning. It contains papers on major aspects of statistical and neural network learning, their links to biological learning and nonlinear dynamics (chaos), and real-lif…e examples of pattern recognition applications.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 416 pp. Englisch.