Download or read book Classification probabiliste par reseaux de neurones Application a la reconnaissance de l ecriture manuscrite written by David Price and published by . This book was released on 1996 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book RESEAUX DE NEURONES POUR LA CLASSIFICATION AUTOMATIQUES APPLICATION A LA RECONNAISSANCE DE CHIFFRES MANUSCRITS written by STEFAN.. KNERR and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: MON TRAVAIL DE THESE A PORTE SUR L'UTILISATION DES RESEAUX DE NEURONES FORMELS DANS LE DOMAINE DE LA CLASSIFICATION AUTOMATIQUE, AVEC POUR APPLICATION PRINCIPALE LA RECONNAISSANCE DES CHIFFRES MANUSCRITS. L'APPORT DE CE TRAVAIL A ETE L'ELABORATION D'UNE PROCEDURE QUI, ETANT DONNE UN PROBLEME DE CLASSIFICATION, TROUVE AUTOMATIQUEMENT UN RESEAU BIEN ADAPTE A LA COMPLEXITE DU PROBLEME A RESOUDRE. CETTE PROCEDURE PERMET D'EVITER LES TATONNEMENTS NECESSAIRES HABITUELLEMENT POUR CONCEVOIR UN CLASSIFIEUR NEURONAL. DE PLUS, ELLE UTILISE DES NEURONES BINAIRES, DONC FACILES A REALISER ELECTRONIQUEMENT, ET, SURTOUT ELLE DONNE DES INDICATIONS SUR LA COMPLEXITE DE LA TACHE DE CLASSIFICATION QUE L'ON CHERCHE A EFFECTUER. CETTE PROCEDURE A ETE APPLIQUEE A DES PROBLEMES MODELES, AINSI QU'AU PROBLEME REEL DE LA RECONNAISSANCE DE CHIFFRES MANUSCRITS. POUR LES DEUX BASES DE DONNEES UTILISEES, L'UNE D'ORIGINE EUROPEENNE ET L'AUTRE PROVENANT DE CODES POSTAUX AMERICAINS, LA PROCEDURE A FOURNI UNE ARCHITECTURE DE RESEAU RELATIVEMENT SIMPLE PAR RAPPORT A DES RESEAUX PROPOSES, POUR EFFECTUER LA MEME TACHE, PAR D'AUTRES EQUIPES. LES PERFORMANCES OBTENUES AVEC CE RESEAU SONT SATISFAISANTES ET TOUT A FAIT COMPARABLES AUX RESULTATS OBTENUS A L'AIDE DE RESEAUX PLUS COMPLEXES. ELLES ONT JUSTIFIE LA REALISATION DE CE RESEAU SOUS LA FORME D'UN CIRCUIT INTEGRE SPECIFIQUE, DONT L'ARCHITECTURE SERA PRESENTEE
Download or read book Reconnaissance de caract res industriels par application d un syst me de r seaux de neurones boucle de r troaction written by Stéphane Lecœuche and published by . This book was released on 1998 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: La reconnaissance de caracteres en milieu industriel est confrontee aux problemes de la variete des supports utilises (metal, bois, papier,) et du type d'ecriture (jet d'encre, gravure,). L'etude presentee porte sur la conception d'un systeme de reconnaissance de caracteres qui possede les proprietes d'autoadaptation necessaires a la resolution de ces problemes. Le systeme propose est elabore a partir de deux reseaux de neurones et d'une boucle de retroaction. Le premier reseau permet d'extraire un modele binarise invariant quels que soient le materiau et le type de marquage et quelles que soient la position, la taille et l'orientation du caractere. Il est construit suivant une architecture a couches structurees decrivant une operation de vision et une operation de normalisation. Un second reseau supervise du type rbf realise la classification de ce modele. Ce reseau a ete concu pour permettre et faciliter la reconnaissance multifonte en prenant compte de la variete et de la specificite des polices utilisees en marquage industriel. En fonction de la qualite de reconnaissance, le systeme peut grace a la boucle de retroaction, apporter au premier reseau l'information necessaire a l'amelioration de la vision des caracteres. La premiere partie est une introduction a la reconnaissance de caracteres et aux systemes d'ocr, nous presentons ensuite en deuxieme partie une bibliographie sur les reseaux de neurones utilises en classification et en troisieme partie quelques reseaux de neurones utilises en reconnaissance invariante. Dans une quatrieme partie, nous presentons l'architecture de notre systeme et detaillons les concepts originaux tels que la notion de centre/voisinage developpee dans l'etage de vision, l'architecture de normalisation et la notion de forme introduite dans la phase d'apprentissage du reseau rbf. La cinquieme partie est consacree a l'analyse des performances du systeme applique a des cas reels.
Download or read book Un r seau de neurones pour la classification d images written by Anne Trotin and published by . This book was released on 1993 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: L'UN DES PRINCIPAUX AVANTAGES DES RESEAUX DE NEURONES EST LE PARALLELISME INHERENT A L'ORGANISATION DES TRAITEMENTS. JUSQU'A PRESENT, LA PLUPART DES MODELES ETANT SIMULES SUR DES MACHINES CONVENTIONNELLES, CE CRITERE N'EST PAS EXPLOITE. L'UN DES INTERETS D'UNE REALISATION MATERIELLE EST DONC D'AUGMENTER LES VITESSES DE TRAITEMENT. CETTE THESE TENTE DE MONTRER LA FAISABILITE DE L'IMPLANTATION D'UN ALGORITHME PARTICULIER DE RESEAU DE NEURONES SUR UN CIRCUIT NUMERIQUE VLSI. L'APPLICATION VISEE EST LA RECONNAISSANCE D'IMAGES. LE NEOCOGNITRON A ETE RETENU CAR IL POSSEDE NON SEULEMENT DES CAPACITES PARTICULIEREMENT INTERESSANTES DE TOLERANCE AUX DEFORMATIONS DES IMAGES EN PHASE DE GENERALISATION, MAIS EGALEMENT UNE STRUCTURE A CONNEXIONS LOCALES ET A POIDS PARTAGES QUI LE DESIGNE COMME UN BON CANDIDAT A UNE INTEGRATION MATERIELLE. LE MODELE A TOUT D'ABORD ETE ETUDIE DE FACON GENERALE. CETTE ETAPE A CONDUIT A EFFECTUER DIVERSES TRANSFORMATIONS SUR L'ALGORITHME DE DEPART AFIN DE REDUIRE LA COMPLEXITE DES TRAITEMENTS EN PHASE DE FONCTIONNEMENT. L'APPRENTISSAGE A EGALEMENT ETE MODIFIE, CE QUI A PERMIS DE DETERMINER EN PARTIE DE FACON AUTOMATIQUE LA TAILLE DES DIFFERENTES COUCHES DU RESEAU. CE TRAVAIL A ETE VALIDE PAR DES SIMULATIONS SUR UNE BASE DE DONNEES DE CHIFFRES MANUSCRITS EXTRAITS DES CODES POSTAUX. LES RESULTATS OBTENUS MONTRENT QUE LES SIMPLIFICATIONS EFFECTUEES, LOIN DE DEGRADER LES PERFORMANCES, LES AMELIORENT MEME PARFOIS. L'ETUDE DE L'ARCHITECTURE D'UN CIRCUIT INTEGRE NUMERIQUE A PERMIS D'EVALUER LA COMPLEXITE DU PROBLEME. IL APPARAIT QUE LE NEOCOGNITRON APRES MODIFICATIONS PEUT ETRE IMPLANTE. CELA DEMANDE NEANMOINS LE CHOIX D'UNE APPLICATION DONNEE QUI PERMET DE DEFINIR UNE STRUCTURE ADAPTEE. LA REALISATION PHYSIQUE NE SE RESTREINT PAS POUR AUTANT A UN UNIQUE RESEAU CAR CERTAINS PARAMETRES RESTENT PROGRAMMABLES.
Download or read book ARCHITECTURES ET APPRENTISSAGE DE RESEAUX DE NEURONES POUR LA CLASSIFICATION ET POUR LA PREDICTION DE SUITES CHRONOLOGIQUES written by ISABELLE.. POUJAUD and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: L'ETUDE PRESENTEE DANS CE MEMOIRE TRAITE DE DEUX APPLICATIONS DES RESEAUX DE NEURONES: LA CLASSIFICATION DE FORMES STATIQUES ET LA PREDICTION DE SUITES CHRONOLOGIQUES. LE MEMOIRE DEBUTE PAR UNE PRESENTATION GENERALE DES ARCHITECTURES DE RESEAUX ET DES ALGORITHMES D'APPRENTISSAGE S'Y RAPPORTANT. LA DEUXIEME PARTIE EST CONSACREE A L'ETUDE DES CLASSIFIEURS NEURONAUX. NOUS PRESENTONS LA FACON DONT UN RESEAU DE NEURONES PERMET D'ETABLIR DES SEPARATIONS ENTRE DES ENSEMBLES DE POINTS REPRESENTATIFS DE CLASSES. NOUS TRAITONS ENSUITE DES PROBLEMES SIMPLES QUI NOUS PERMETTENT DE METTRE EN EVIDENCE LES DIFFERENTS PARAMETRES INTERVENANT DANS LA RESOLUTION DE CES PROBLEMES. PUIS NOUS TRAITONS UN PROBLEME REEL ET COMPLEXE: LA RECONNAISSANCE DE CHIFFRES MANUSCRITS. NOUS TERMINONS PAR LA PRESENTATION D'UNE METHODE DE PLUS EN PLUS UTILISEE POUR LA REALISATION DE CLASSIFIEURS. ELLE CONSISTE A CONSTRUIRE LE RESEAU AU FUR ET A MESURE DES BESOINS INDIQUES PAR LES PERFORMANCES DE CLASSIFICATION OBTENUES. LA TROISIEME PARTIE EST CONSACREE A LA REALISATION DE PREDICTEURS NON LINEAIRES POUR DES SUITES CHRONOLOGIQUES. LE PROBLEME INITIAL CONSISTAIT A TROUVER UN PREDICTEUR POUR UNE SUITE CONSTITUEE DE DONNEES REELLES: LES COURS BOURSIERS. AFIN D'INTERPRETER LA FACON DONT UN RESEAU DE NEURONES PEUT ASSURER LA FONCTION D'UN PREDICTEUR, NOUS AVONS TRAITE EN PARALLELE DES SUITES MODELES. AVANT DE RECHERCHER DES PREDICTEURS, NOUS AVONS PROCEDE A UNE ANALYSE DES DIFFERENTES SUITES AFIN DE LES CARACTERISER. NOUS AVONS ENSUITE TESTE DES PREDICTEURS POLYNOMIAUX AINSI QUE DES PREDICTEURS NEURONAUX. LES PREDICTEURS TESTES N'ONT PAS FOURNI LES RESULTATS ATTENDUS POUR LES SUITES ISSUES DES COURS BOURSIERS, EN REVANCHE, LES PERFORMANCES OBTENUES AVEC LES SUITES MODELES MONTRENT QUE LES RESEAUX DE NEURONES SONT DES OUTILS CAPABLES D'APPRENDRE DES FONCTIONS ET DE REALISER AINSI DE BONS PREDICTEURS
Download or read book Contribution l tude des r seaux de neurones formels pour la reconnaissance des formes written by Yizhak Idan and published by . This book was released on 1992 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: NOUS ETUDIONS CERTAINS APPORTS DES TECHNIQUES NEURONALES A LA RECONNAISSANCE DES FORMES. NOUS AVONS D'ABORD EVALUE LES POSSIBILITES D'APPLICATION D'UNE MEMOIRE ASSOCIATIVE QUI A ETE IMPLANTEE OPTIQUEMENT DANS NOTRE LABORATOIRE. LE MANQUE D'AVANTAGES DECISIFS EN FAVEUR DES ARCHITECTURES DE TYPE HOPFIELD NOUS A INCITE A TRAVAILLER SUR DES MODELES STATISTIQUES DE CLASSIFICATION, APPLIQUES A DES DONNEES DE TYPES CHIFFRES MANUSCRITS. LE MODELE DE KOHONEN NOUS SEMBLE COMPLEMENTAIRE DES METHODES LOCALES ET, DE PLUS, APPROPRIE A L'IMPLANTATION OPTIQUE A MOYEN TERME. NOUS AVONS PU PROFITER D'UN TRAVAIL SUR LES MODELES D'ASSOCIATION SYMBOLIQUE PAR CARTES DE KOHONEN: LE MODELE LASSO. TRAITANT LA CLASSIFICATION COMME UN PROBLEME D'ASSOCIATION DISCRETE, NOUS AVONS PROPOSE UNE SUPERVISION FORTE, NOTAMMENT PAR LA NORMALISATION DE DISTANCES. POUR L'APPLICATION SPECIFIQUE AU TRI POSTAL, ON PREFERE LE REJET DE BONNES REPONSES A L'ACCEPTATION DE REPONSES FAUSSES; NOUS AVONS ALORS UTILISE UN RAYON DE COOPERATION, QUI FACILITE LES REJETS PAR UN POST-TRAITEMENT. NOUS AVONS EVALUE D'AUTRES METHODES COMME: LES K-PLUS-PROCHES-VOISINS, LES FENETRES DES PARZEN, LVQ, LES CARTES DE KOHONEN, LE NEOCOGNITRON ET UN MODELE DE TYPE TDNN QUI A ETE SUGGERE COMME LE MEILLEUR POUR CETTE TACHE. LE TAUX DE RECONNAISSANCE DES METHODES NEURONALES ETUDIEES EST COMPARABLE A CELUI DES TECHNIQUES STATISTIQUES NON PARAMETRIQUES ETUDIEES, ET CES METHODES PERMETTENT UN GAIN EN PLACE MEMOIRE ET EN TEMPS DE CALCUL. TDNN ET LASSO TRAITANT L'INFORMATION DE MANIERE COMPLEMENTAIRE, NOUS AVONS HYBRIDE CES MODELES EN PARALLELE DANS UN SIMPLE POST-TRAITEMENT NEURONAL. NOUS AVONS MONTRE QUE LES PERFORMANCES OBTENUES PAR UNE TELLE COOPERATION SONT SENSIBLEMENT MEILLEURES QUE CELLES DES MODELES TRAVAILLANT SEPAREMENT. CELA TEND A DEMONTRER L'AVANTAGE DE L'UTILISATION DE RESEAUX DE NEURONES POUR L'EXTRACTION D'INFORMATIONS COMPLEMENTAIRES, COMME DANS DES APPLICATIONS DE FUSION DE DONNEES.
Download or read book RECONNAISSANCE DE CARACTERES MANUSCRITS PAR COMBINAISON DE MODELES CONNEXIONNISTES written by Bertrand Lamy and published by . This book was released on 1995 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: LA RECONNAISSANCE DE CARACTERES MANUSCRITS EST UN DOMAINE TRES ACTIF DE LA RECHERCHE EN INFORMATIQUE: LA VARIABILITE DE L'ECRITURE MANUSCRITE PERMET EN EFFET DE CONFRONTER LES ALGORITHMES DE CLASSIFICATION ET D'APPRENTISSAGE A DES PROBLEMES DIFFICILES ET REALISTES. LES RESEAUX DE NEURONES ONT MONTRE DES RESULTATS REMARQUABLES DANS CE DOMAINE, MAIS LA NECESSITE DE PERFORMANCES ELEVEES DANS LES APPLICATIONS REELLES POUSSE LA RECHERCHE VERS DES MODELES CONNEXIONNISTES DE PLUS EN PLUS COMPLEXES. CETTE THESE PROPOSE DES ALTERNATIVES POSSIBLES DANS LA CONCEPTION DE SYSTEMES DE CLASSIFICATION AUTOMATIQUE. DANS UN PREMIER TEMPS, NOUS MONTRONS QUE L'UTILISATION DE MODELES PLUS SIMPLES, BIEN QU'ELLE NE PERMETTE PAS D'ATTEINDRE DES PERFORMANCES COMPARABLES A CELLES DES MEILLEURS SYSTEMES ACTUELS, OFFRE CEPENDANT UN PREMIER COMPROMIS ENTRE PERFORMANCE ET RAPIDITE DE CALCUL. NOUS PRESENTONS ENSUITE UNE ETUDE DETAILLEE D'UNE APPROCHE RECEMMENT UTILISEE EN CLASSIFICATION: LA COMBINAISON DE MODELES. NOUS MONTRONS QUE L'INDEPENDANCE DES MODELES IMPLIQUES DANS UN TEL SCHEMA EST UNE CONDITION NECESSAIRE A L'AMELIORATION DES PERFORMANCES. EN PARTICULIER, SUR LE PROBLEME DE LA RECONNAISSANCE DES CARACTERES, NOUS MONTRONS QUE DANS LE CAS DE RESEAUX IDENTIQUES, A POIDS INITIAUX DIFFERENTS, L'INDEPENDANCE N'EST PAS SYSTEMATIQUE: LE GAIN EN COMBINAISON PEUT ETRE NUL. POUR CLARIFIER LA NECESSITE D'INDEPENDANCE, NOUS MONTRONS QUE LA COMBINAISON DE DEUX SYSTEMES CONSTRUITS A PARTIR DE CODAGES DIFFERENTS ET D'ARCHITECTURES DIFFERENTES, ARRIVE A PRODUIRE DES PERFORMANCES DE TRES HAUT NIVEAU. UNE ETUDE COMPARATIVE DE LA CAPACITE DE REJET DE CETTE METHODE NOUS PERMET DE CONCLURE QU'ELLE SURPASSE CERTAINES APPROCHES EXISTANTES
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 Life Insurance Mathematics written by Hans U. Gerber and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: HaIley's Comet has been prominently displayed in many newspapers during the last few months. For the first time in 76 years it appeared this winter, clearly visible against the nocturnal sky. This is an appropriate occasion to point out the fact that Sir Edmund Halley also constructed the world's first life table in 1693, thus creating the scientific foundation of life insurance. Halley's life table and its successors were viewed as deterministic laws, i. e. the number of deaths in any given group and year was considered to be a weIl defined number that could be calculated by means of a life table. However, in reality this number is random. Thus any mathematical treatment of life insurance will have to rely more and more on prob ability theory. By sponsoring this monograph the Swiss Association of Actuaries wishes to support the "modern" probabilistic view oflife contingencies. We are fortu nate that Professor Gerber, an internationally renowned expert, has assumed the task of writing the monograph. We thank the Springer-Verlag and hope that this monograph will be the first in a successful series of actuarial texts. Hans Bühlmann Zürich, March 1986 President Swiss Association of Actuaries Preface Two major developments have influenced the environment of actuarial math ematics. One is the arrival of powerful and affordable computers; the once important problem of numerical calculation has become almost trivial in many instances.
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 Fluorine Magnetic Resonance Imaging written by Ulrich Flogel and published by CRC Press. This book was released on 2016-10-26 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, fluorine (19F) magnetic resonance imaging (MRI) has garnered significant scientific interest in the biomedical research community owing to the unique properties of fluorinated materials and the 19F nucleus. Fluorine has an intrinsically sensitive nucleus for MRI. There is negligible endogenous 19F in the body and thus there is no background signal. Fluorine-containing compounds are ideal tracer labels for a wide variety of MRI applications. Moreover, the chemical shift and nuclear relaxation rate can be made responsive to physiology via creative molecular design. This book is an interdisciplinary compendium that details cutting-edge science and medical research in the emerging field of 19F MRI. Edited by Ulrich Flögel and Eric Ahrens, two prominent MRI researchers, this book will appeal to investigators involved in MRI, biomedicine, immunology, pharmacology, probe chemistry, and imaging physics.
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 Complexity Science and Society written by Jan Bogg and published by Radcliffe Publishing. This book was released on 2007 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complexity is a new interdisciplinary approach to science and society that challenges traditional academic divisions, frameworks and paradigms. This book helps the expert, student or policy practitioner have a better understanding of the enormous potential of complexity, and how it relates to their particular area of interest or expertise.
Download or read book Collaboration with Parents of Exceptional Children written by Marvin J. Fine and published by . This book was released on 1991 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Analysis in Natural Language Processing written by Shay Cohen and published by Springer Nature. This book was released on 2022-11-10 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.
Download or read book Machine Translation written by Thierry Poibeau and published by MIT Press. This book was released on 2017-09-15 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry. The dream of a universal translation device goes back many decades, long before Douglas Adams's fictional Babel fish provided this service in The Hitchhiker's Guide to the Galaxy. Since the advent of computers, research has focused on the design of digital machine translation tools—computer programs capable of automatically translating a text from a source language to a target language. This has become one of the most fundamental tasks of artificial intelligence. This volume in the MIT Press Essential Knowledge series offers a concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and market potential. The main approaches are presented from a largely historical perspective and in an intuitive manner, allowing the reader to understand the main principles without knowing the mathematical details. The book begins by discussing problems that must be solved during the development of a machine translation system and offering a brief overview of the evolution of the field. It then takes up the history of machine translation in more detail, describing its pre-digital beginnings, rule-based approaches, the 1966 ALPAC (Automatic Language Processing Advisory Committee) report and its consequences, the advent of parallel corpora, the example-based paradigm, the statistical paradigm, the segment-based approach, the introduction of more linguistic knowledge into the systems, and the latest approaches based on deep learning. Finally, it considers evaluation challenges and the commercial status of the field, including activities by such major players as Google and Systran.
