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Titre : Introduction to Artificial Intelligence Type de document : Monographie Auteurs : Wolfgang Ertel, Auteur Editeur : Springer International Publishing Année de publication : 2017 Importance : 365 p. ISBN/ISSN/EAN : 978-3-319-58487-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification bayesienne
[Termes IGN] entropie maximale
[Termes IGN] exploration de données
[Termes IGN] PROLOG
[Termes IGN] raisonnement sémantique
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website. Note de contenu : 1- Introduction
2- Propositional Logic
3- First-order Predicate Logic
4- Limitations of Logic
5- Logic Programming with PROLOG
6- Search, Games and Problem Solving
7- Reasoning with Uncertainty
8- Machine Learning and Data Mining
9- Neural Networks
10- Reinforcement Learning
11- Solutions for the ExercisesNuméro de notice : 25753 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-58487-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94945
Titre : Robotics, vision and control Type de document : Monographie Auteurs : Peter Corke, Auteur Editeur : Springer International Publishing Année de publication : 2017 Importance : 570 p. Format : 21 x 27 cm ISBN/ISSN/EAN : 978-3-319-54413-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] Matlab
[Termes IGN] navigation inertielle
[Termes IGN] robot mobile
[Termes IGN] robotique
[Termes IGN] traitement d'image
[Termes IGN] vision par ordinateur
[Termes IGN] vitesse radialeRésumé : (éditeur) Robotic vision, the combination of robotics and computer vision, involves the application of computer algorithms to data acquired from sensors. The research community has developed a large body of such algorithms but for a newcomer to the field this can be quite daunting. For over 20 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in an accessible but informative style, easy to read and absorb, and includes over 1000 MATLAB and Simulink® examples and over 400 figures. The book is a real walk through the fundamentals of mobile robots, arm robots. then camera models, image processing, feature extraction and multi-view geometry and finally bringing it all together with an extensive discussion of visual servo systems. This second edition is completely revised, updated and extended with coverage of Lie groups, matrix exponentials and twists; inertial navigation; differential drive robots; lattice planners; pose-graph SLAM and map making; restructured material on arm-robot kinematics and dynamics; series-elastic actuators and operational-space control; Lab color spaces; light field cameras; structured light, bundle adjustment and visual odometry; and photometric visual servoing. Note de contenu : 1- Introduction
2- Representing Position and Orientation
3- Time and Motion
4- Mobile Robot Vehicles
5- Navigation
6- Localization
7- Robot Arm Kinematics
8- Manipulator Velocity
9- Dynamics and Control
10- Light and Color
11- Image Formation
12- Images and Image Processing
13- Image Feature Extraction
14- Using Multiple Images
15- Vision-Based Control
16- Advanced Visual ServoingNuméro de notice : 25794 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-54413-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95024 Sparsity, redundancy and robustness in artificial neural networks for learning and memory / Philippe Tigréat (2017)
Titre : Sparsity, redundancy and robustness in artificial neural networks for learning and memory Type de document : Thèse/HDR Auteurs : Philippe Tigréat, Auteur ; Claude Berrou, Directeur de thèse Editeur : Institut Mines-Télécom Atlantique IMT Atlantique Année de publication : 2017 Autre Editeur : Université Bretagne Loire Importance : 150 P. Format : 21 x 30 cm Note générale : bibliographie
Thèse IMT Atlantique sous le sceau de l’Université Bretagne Loire pour obtenir le grade de Docteur, Signal, Image, VisionLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage (cognition)
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] codage
[Termes IGN] cognition
[Termes IGN] mémoire
[Termes IGN] reconnaissance de formes
[Termes IGN] stockage de donnéesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The objective of research in Artificial Intelligence (AI) is to reproduce human cognitive abilities by means of modern computers. The results of the last few years seem to announce a technological revolution that could profoundly change society. We focus our interest on two fundamental cognitive aspects, learning and memory. Associative memories offer the possibility to store information elements and to retrieve them using a sub-part of their content, thus mimicking human memory. Deep Learning allows to transition from an analog perception of the outside world to a sparse and more compact representation.In Chapter 2, we present a neural associative memory model inspired by Willshaw networks, with constrained connectivity. This brings an performance improvement in message retrieval and a more efficient storage of information.In Chapter 3, a convolutional architecture was applied on a task of reading partially displayed words under similar conditions as in a former psychology study on human subjects. This experiment put inevidence the similarities in behavior of the network with the human subjects regarding various properties of the display of words.Chapter 4 introduces a new method for representing categories usingneuron assemblies in deep networks. For problems with a large number of classes, this allows to reduce significantly the dimensions of a network.Chapter 5 describes a method for interfacing deep unsupervised networks with clique-based associative memories. Note de contenu : 1- Introduction
2- Sparse Neural Associative Memories
3- Robustness of Deep Neural Networks to Erasures in a Reading Task
4- Assembly Output Codes for Learning Neural Networks
5- Combination of Unsupervised Learning and Associative Memory
6- Conclusion and OpeningsNuméro de notice : 25836 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Signal, Image, Vision : Mines-Télécom Atlantique : 2017 Organisme de stage : Laboratoire Labsticc nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-01812053 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95178 Prediction of categorical spatial data via Bayesian updating / Xiang Huang in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)
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Titre : Prediction of categorical spatial data via Bayesian updating Type de document : Article/Communication Auteurs : Xiang Huang, Auteur ; Zhizhong Wang, Auteur ; Jianhua Guo, Auteur Année de publication : 2016 Article en page(s) : pp 1426 - 1449 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse spatiale
[Termes IGN] classification dirigée
[Termes IGN] mise à jour automatique
[Termes IGN] système expertRésumé : (Auteur) This study introduces a transition probability-based Bayesian updating (BU) approach for spatial classification through expert system. Transition probabilities are interpreted as expert opinions for updating the prior marginal probabilities of categorical response variables. The main objective of this paper is to provide a spatial categorical variable prediction method which has a solid theoretical foundation and yields relatively higher classification accuracy compared with conventional ones. The basic idea is to first build a linear Bayesian updating (LBU) model that corresponds to an application of Bayes’ theorem. Since the linear opinion pool is intrinsically suboptimal and underconfident, the beta-transformed Bayesian updating (BBU) model is proposed to overcome this limitation. Another type of BU approach, conditional independent Bayesian updating (CIBU), is derived based on conditional independent experts. It is shown that traditional Markovian-type categorical prediction (MCP) is equivalent to a particular CIBU model with specific parameters. As three variants of the BU method, these techniques are illustrated in synthetic and real-world case studies, comparison results with both the LBU and MCP favor the BBU model. Numéro de notice : A2016-310 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1133819 En ligne : http://dx.doi.org/10.1080/13658816.2015.1133819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80910
in International journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1426 - 1449[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2016042 RAB Revue Centre de documentation En réserve L003 Disponible 079-2016041 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Deep learning Type de document : Guide/Manuel Auteurs : Ian Goodfellow, Auteur ; Yoshua Bengio, Auteur ; Aaron Courville, Auteur Editeur : Cambridge [Massachusetts - Etats-Unis] : MIT Press Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algèbre linéaire
[Termes IGN] apprentissage profond
[Termes IGN] inférence
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. Numéro de notice : 17527 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel de cours En ligne : http://www.deeplearningbook.org Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90631 PermalinkUn environnement collaboratif pour l’acquisition de compétences en conception-développement d’applications centrées utilisateur. Application aux systèmes d'assitance à la santé et au bien-être / Maha Khemaja in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)PermalinkHow much past to see the future: a computational study in calibrating urban cellular automata / Ivan Blecic in International journal of geographical information science IJGIS, vol 29 n° 3 (March 2015)PermalinkPermalinkEn route vers le Web sémantique ? / Françoise de Blomac in DécryptaGéo le mag, n° 161 (01/11/2014)PermalinkPermalinkJFSMA'14 systèmes multi-agents : principe de parcimonie, 8-10 octobre 2014, Loriol-sur-Drôme / Rémy Courdier (2014)PermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 1. Représentation des connaissances et formalisation des raisonnements / Pierre Marquis (2014)PermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 2. Algorithmes pour l'intelligence artificielle / Pierre Marquis (2014)PermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 3. L'intelligence artificielle : frontières et applications / Pierre Marquis (2014)Permalink