Download or read book Methodes de geometrie differentielle dans les modeles statistiques et applications modeles exponentiels et modeles normaux multidimensionnels reconstruction des densites de probabilite et des densites spectrales written by F. El Abdi and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Methodes de geometrie differentielle dans les modeles statistiques written by Fouad el Abdi and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book G om trie diff rentielle written by Marcel Berger and published by Presses Universitaires de France - PUF. This book was released on 1992 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book APPLICATION DES METHODES DE LA GEOMETRIE DIFFERENTIELLE POUR L ETUDE AU SECOND ORDRE D ESTIMATEURS DANS LE MODELE POLYGENIQUE EN GENETIQUE written by Mohammed El Hafed Deheb and published by . This book was released on 1990 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: DANS CETTE THESE NOUS TESTONS SUR UN MODELE PARTICULIER DIVERSES METHODES STATISTIQUES RELATIVEMENT NOUVELLES : D'UNE PART LES METHODES UTILISANT DES DEVELOPPEMENTS AU SECOND ORDRE, QUE L'ON ECLAIRER PAR UNE INTERPRETATION GEOMETRIQUE, D'AUTRE PART DES METHODES DE REECHANTILLONNAGE. NOUS NOUS SOMMES INTERESSES AU MODELE GENETIQUE APPELE MODELE POLYGENIQUE. CE MODELE EST DECRIT EN DETAIL DANS LE CHAPITRE 1. AU CHAPITRE 2, NOUS INTRODUISONS LES OUTILS DE STATISTIQUE ET DE GEOMETRIE DIFFERENTIELLE DONT NOUS AURONS BESOIN. UNE ETUDE PAR SIMULATION (CHAPITRE 3) A MONTRE LA MAUVAISE QUALITE DE L'ESTIMATEUR DE MAXIMUM DE VRAISEMBLANCE (EMV). IL SE TROUVE QUE DANS LE MODELE ETUDIE ICI, L'INFORMATION DE FISHER I EST GENERALEMENT PROCHE DE LA DEGENERESCENCE. IL EXISTE UNE DIRECTION (CELLE ASSOCIEE A LA PLUS PETITE VALEUR PROPRE DE I) DANS LAQUELLE L'ESTIMATION EST MAUVAISE. CETTE SITUATION EST TYPIQUEMENT CELLE POUR LAQUELLE LES CORRECTIONS AU SECOND ORDRE SONT DELICATES. LA METHODE DE REECHANTILLONNAGE DONNE DES RESULTATS PLUS SATISFAISANTS MAIS DEMANDE UN TEMPS DE CALCUL EXCESSIF. POUR RESOUDRE CE PROBLEME NOUS AVONS CHERCHE A CONTOURNER LA DEGENERESCENCE DE I EN INTRODUISANT UN MODELE A UN SEUL PARAMETRE. POUR CE MODELE A UN SEUIL PARAMETRE, IL N'Y A PLUS DEGENERESCENCE DE I. ON EST DANS UN MODELE REGULIER
Download or read book Le ons et applications de g om trie diff rentielle et de m canique analytique written by Yves Talpaert and published by Editions Cépaduès. This book was released on 1993 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Predicting Structured Data written by Neural Information Processing Systems Foundation and published by MIT Press. This book was released on 2007 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Download or read book An Introduction to Computational Learning Theory written by Michael J. Kearns and published by MIT Press. This book was released on 1994-08-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Download or read book The Nature of Statistical Learning Theory written by Vladimir Vapnik and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Download or read book Sampling in Combinatorial and Geometric Set Systems written by Nabil H. Mustafa and published by American Mathematical Society. This book was released on 2022-01-14 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the behavior of basic sampling techniques and intrinsic geometric attributes of data is an invaluable skill that is in high demand for both graduate students and researchers in mathematics, machine learning, and theoretical computer science. The last ten years have seen significant progress in this area, with many open problems having been resolved during this time. These include optimal lower bounds for epsilon-nets for many geometric set systems, the use of shallow-cell complexity to unify proofs, simpler and more efficient algorithms, and the use of epsilon-approximations for construction of coresets, to name a few. This book presents a thorough treatment of these probabilistic, combinatorial, and geometric methods, as well as their combinatorial and algorithmic applications. It also revisits classical results, but with new and more elegant proofs. While mathematical maturity will certainly help in appreciating the ideas presented here, only a basic familiarity with discrete mathematics, probability, and combinatorics is required to understand the material.
Download or read book Random Trees written by Michael Drmota and published by Springer Science & Business Media. This book was released on 2009-04-16 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide a thorough introduction to various aspects of trees in random settings and a systematic treatment of the mathematical analysis techniques involved. It should serve as a reference book as well as a basis for future research.
Download or read book Handbook of Nonlinear Partial Differential Equations written by Andrei D. Polyanin and published by CRC Press. This book was released on 2004-06-02 with total page 835 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Nonlinear Partial Differential Equations is the latest in a series of acclaimed handbooks by these authors and presents exact solutions of more than 1600 nonlinear equations encountered in science and engineering--many more than any other book available. The equations include those of parabolic, hyperbolic, elliptic and other types, and the authors pay special attention to equations of general form that involve arbitrary functions. A supplement at the end of the book discusses the classical and new methods for constructing exact solutions to nonlinear equations. To accommodate different mathematical backgrounds, the authors avoid wherever possible the use of special terminology, outline some of the methods in a schematic, simplified manner, and arrange the equations in increasing order of complexity. Highlights of the Handbook:
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Download or read book Introduction to Nonsmooth Optimization written by Adil Bagirov and published by Springer. This book was released on 2014-08-12 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily differentiable optimization). Solving these kinds of problems plays a critical role in many industrial applications and real-world modeling systems, for example in the context of image denoising, optimal control, neural network training, data mining, economics and computational chemistry and physics. The book covers both the theory and the numerical methods used in NSO and provide an overview of different problems arising in the field. It is organized into three parts: 1. convex and nonconvex analysis and the theory of NSO; 2. test problems and practical applications; 3. a guide to NSO software. The book is ideal for anyone teaching or attending NSO courses. As an accessible introduction to the field, it is also well suited as an independent learning guide for practitioners already familiar with the basics of optimization.
Download or read book Mathematics of the Decision Sciences written by George Bernard Dantzig and published by American Mathematical Soc.. This book was released on 1968-12-31 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonsmooth Optimization written by Claude Lemarechal and published by Elsevier. This book was released on 2014-05-19 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonsmooth Optimization contains the proceedings of a workshop on non-smooth optimization (NSO) held from March 28 to April 8,1977 in Austria under the auspices of the International Institute for Applied Systems Analysis. The papers explore the techniques and theory of NSO and cover topics ranging from systems of inequalities to smooth approximation of non-smooth functions, as well as quadratic programming and line searches. Comprised of nine chapters, this volume begins with a survey of Soviet research on subgradient optimization carried out since 1962, followed by a discussion on rates of convergence in subgradient optimization. The reader is then introduced to the method of subgradient optimization in an abstract setting and the minimal hypotheses required to ensure convergence; NSO and nonlinear programming; and bundle methods in NSO. A feasible descent algorithm for linearly constrained least squares problems is described. The book also considers sufficient minimization of piecewise-linear univariate functions before concluding with a description of the method of parametric decomposition in mathematical programming. This monograph will be of interest to mathematicians and mathematics students.
Download or read book Fixed Point Theorems with Applications to Economics and Game Theory written by Kim C. Border and published by Cambridge University Press. This book was released on 1985 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores fixed point theorems and its uses in economics, co-operative and noncooperative games.
Download or read book Analytic Combinatorics written by Philippe Flajolet and published by Cambridge University Press. This book was released on 2009-01-15 with total page 825 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analytic combinatorics aims to enable precise quantitative predictions of the properties of large combinatorial structures. The theory has emerged over recent decades as essential both for the analysis of algorithms and for the study of scientific models in many disciplines, including probability theory, statistical physics, computational biology, and information theory. With a careful combination of symbolic enumeration methods and complex analysis, drawing heavily on generating functions, results of sweeping generality emerge that can be applied in particular to fundamental structures such as permutations, sequences, strings, walks, paths, trees, graphs and maps. This account is the definitive treatment of the topic. The authors give full coverage of the underlying mathematics and a thorough treatment of both classical and modern applications of the theory. The text is complemented with exercises, examples, appendices and notes to aid understanding. The book can be used for an advanced undergraduate or a graduate course, or for self-study.
