Download or read book Etude de propri t s d apprentissage supervis et non supervis par des m thodes de physique statistique written by Arnaud Buhot and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: L'objet de cette these est l'etude de diverses proprietes d'apprentissage a partir d'exemples par des methodes de physique statistique, notamment, par la methode des repliques. Des taches supervisees, correspondant a la classification binaire de donnees, ainsi que des taches non supervisees, comme l'estimation parametrique d'une densite de probabilite, sont considerees. Dans la premiere partie, une approche variationnelle permet de determiner la performance de l'apprentissage optimal d'une direction d'anisotropie, et de deduire une fonction de cout permettant d'obtenir ces performances optimales. Dans le cas de l'apprentissage supervise d'une tache lineairement separable, des simulations numeriques confirmant nos resultats theoriques ont permis de determiner les effets de taille finie. Dans le cas d'une densite de probabilite constituee de deux gaussiennes, la performance de l'apprentissage optimal presente de nombreuses transitions de phases en fonction du nombre de donnees. Ces resultats soulevent une controverse entre la theorie variationnelle et l'approche bayesienne de l'apprentissage optimal. Dans la deuxieme partie, nous etudions deux approches differentes de l'apprentissage de taches de classification complexes. La premiere approche consideree est celle des machines a exemples supports. Nous avons etudie une famille de ces machines pour laquelle les separateurs lineaire et quadratique sont deux cas particuliers. La capacite, les valeurs typiques de la marge et du nombre d'exemples supports, sont determinees. La deuxieme approche consideree est celle d'une machine de parite apprenant avec un algorithme incremental. Cet algorithme construit progressivement un reseau de neurones a une couche cachee. La capacite theorique obtenue pour l'algorithme considere est proche de celle de la machine de parite.
Download or read book ETUDE DE PROPRIETES D APPRENTISSAGE SUPERVISE ET NON SUPERVISE PAR DES METHODES DE PHYSIQUE STATISTIQUE written by ARNAUD.. BUHOT and published by . This book was released on 1999 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: L'OBJET DE CETTE THESE EST L'ETUDE DE DIVERSES PROPRIETES D'APPRENTISSAGE A PARTIR D'EXEMPLES PAR DES METHODES DE PHYSIQUE STATISTIQUE, NOTAMMENT, PAR LA METHODE DES REPLIQUES. DES TACHES SUPERVISEES, CORRESPONDANT A LA CLASSIFICATION BINAIRE DE DONNEES, AINSI QUE DES TACHES NON SUPERVISEES, COMME L'ESTIMATION PARAMETRIQUE D'UNE DENSITE DE PROBABILITE, SONT CONSIDEREES. DANS LA PREMIERE PARTIE, UNE APPROCHE VARIATIONNELLE PERMET DE DETERMINER LA PERFORMANCE DE L'APPRENTISSAGE OPTIMAL D'UNE DIRECTION D'ANISOTROPIE, ET DE DEDUIRE UNE FONCTION DE COUT PERMETTANT D'OBTENIR CES PERFORMANCES OPTIMALES. DANS LE CAS DE L'APPRENTISSAGE SUPERVISE D'UNE TACHE LINEAIREMENT SEPARABLE, DES SIMULATIONS NUMERIQUES CONFIRMANT NOS RESULTATS THEORIQUES ONT PERMIS DE DETERMINER LES EFFETS DE TAILLE FINIE. DANS LE CAS D'UNE DENSITE DE PROBABILITE CONSTITUEE DE DEUX GAUSSIENNES, LA PERFORMANCE DE L'APPRENTISSAGE OPTIMAL PRESENTE DE NOMBREUSES TRANSITIONS DE PHASES EN FONCTION DU NOMBRE DE DONNEES. CES RESULTATS SOULEVENT UNE CONTROVERSE ENTRE LA THEORIE VARIATIONNELLE ET L'APPROCHE BAYESIENNE DE L'APPRENTISSAGE OPTIMAL. DANS LA DEUXIEME PARTIE, NOUS ETUDIONS DEUX APPROCHES DIFFERENTES DE L'APPRENTISSAGE DE TACHES DE CLASSIFICATION COMPLEXES. LA PREMIERE APPROCHE CONSIDEREE EST CELLE DES MACHINES A EXEMPLES SUPPORTS. NOUS AVONS ETUDIE UNE FAMILLE DE CES MACHINES POUR LAQUELLE LES SEPARATEURS LINEAIRE ET QUADRATIQUE SONT DEUX CAS PARTICULIERS. LA CAPACITE, LES VALEURS TYPIQUES DE LA MARGE ET DU NOMBRE D'EXEMPLES SUPPORTS, SONT DETERMINEES. LA DEUXIEME APPROCHE CONSIDEREE EST CELLE D'UNE MACHINE DE PARITE APPRENANT AVEC UN ALGORITHME INCREMENTAL. CET ALGORITHME CONSTRUIT PROGRESSIVEMENT UN RESEAU DE NEURONES A UNE COUCHE CACHEE. LA CAPACITE THEORIQUE OBTENUE POUR L'ALGORITHME CONSIDERE EST PROCHE DE CELLE DE LA MACHINE DE PARITE.
Download or read book tude de propri t s d apprentissage des machines exemples support SVM par des m thodes de physique statistique written by Sebastián Risau-Gusman and published by . This book was released on 2001 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Networks written by Gérard Dreyfus and published by Springer Science & Business Media. This book was released on 2005-11-25 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Download or read book Random Measures Theory and Applications written by Olav Kallenberg and published by Springer. This book was released on 2017-04-12 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering the first comprehensive treatment of the theory of random measures, this book has a very broad scope, ranging from basic properties of Poisson and related processes to the modern theories of convergence, stationarity, Palm measures, conditioning, and compensation. The three large final chapters focus on applications within the areas of stochastic geometry, excursion theory, and branching processes. Although this theory plays a fundamental role in most areas of modern probability, much of it, including the most basic material, has previously been available only in scores of journal articles. The book is primarily directed towards researchers and advanced graduate students in stochastic processes and related areas.
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 Statistical Inference for Ergodic Diffusion Processes written by Yury A. Kutoyants and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.
Download or read book Semiparametric Theory and Missing Data written by Anastasios Tsiatis and published by Springer Science & Business Media. This book was released on 2007-01-15 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
Download or read book Developing Science Mathematics and ICT Education in Sub Saharan Africa written by Wout Ottevanger and published by World Bank Publications. This book was released on 2007-01-01 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developing Science, Mathematics and ICT (SMICT) in Secondary Education is based on country studies from ten Sub-Saharan African countries: Botswana, Burkina Faso, Ghana, Namibia, Nigeria, Senegal, South Africa, Uganda, Tanzania and Zimbabwe, and a literature review. It reveals a number of huge challenges in SMICT education in sub-Saharan Africa: poorly-resourced schools; large classes; a curriculum hardly relevant to the daily lives of students; a lack of qualified teachers; and inadequate teacher education programs. Through examining country case studies, this paper discusses the lessons for improvement of SMICT in secondary education in Africa.
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 Database Anonymization written by Josep Domingo-Ferrer and published by Morgan & Claypool Publishers. This book was released on 2016-01-01 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guarantees they offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.
Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2012 written by Nicholas Ayache and published by Springer. This book was released on 2012-08-28 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 7510, 7511, and 7512 constitutes the refereed proceedings of the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, held in Nice, France, in October 2012. Based on rigorous peer reviews, the program committee carefully selected 252 revised papers from 781 submissions for presentation in three volumes. The third volume includes 79 papers organized in topical sections on diffusion imaging: from acquisition to tractography; image acquisition, segmentation and recognition; image registration; neuroimage analysis; analysis of microscopic and optical images; image segmentation; diffusion weighted imaging; computer-aided diagnosis and planning; and microscopic image analysis.
Download or read book Innovate Bristol written by Sven Boermeester and published by . This book was released on 2019-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Innovate Bristol highlights and celebrates those companies and individuals that are actively working at building a better tomorrow for all. Innovation Ecosystems thrive through the involvement and support of companies and individuals from all industries, which is why the Innovate series not only focuses on the innovators but also those people whom the Innovation Ecosystem, would not be able to thrive without.
Download or read book Practical Time Series Analysis written by Aileen Nielsen and published by O'Reilly Media. This book was released on 2019-09-20 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
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 Disaster risk reduction in school curricula case studies from thirty countries written by and published by UNESCO. This book was released on 2012 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt:
