Bibliothèque de L'IMIST/CNRST

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Bayesian reasoning and machine learning par Barber,, David, Publication : Cambridge | New York Cambridge University Press 2011 . xxiv, 697 pages , "Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online"-- | "Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online"-- 26 cm. Date : 2011 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Computer vision models, learning, and inference par Prince, Simon J. D. Publication : New York Cambridge University Press 2012 . xi, 580 pages , "This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"-- 26 cm. Date : 2012 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Evaluating Learning Algorithms a classification perspective par Japkowicz, Nathalie. Publication : Cambridge | New York Cambridge University Press 2011 . xvi, 406 pages , "The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings"-- | "Technological advances, in recent decades, have made it possible to automate many tasks that previously required signi.cant amounts of manual time, performing regular or repetitive activities. Certainly, computing machines have proven to be a great asset in improving on human speed and e.ciency as well as in reducing errors in these essentially mechanical tasks. More impressively, however, the emergence of computing technologies has also enabled the automation of tasks that require signi.cant understanding of intrinsically human domains that can, in no way, be qualified as merely mechanical. While we, humans, have maintained an edge in performing some of these tasks, e.g. recognizing pictures or delineating boundaries in a given picture, we have been less successful at others, e.g., fraud or computer network attack detection, owing to the sheer volume of data involved, and to the presence of nonlinear patterns to be discerned and analyzed simultaneously within these data. Machine Learning and Data Mining, on the other hand, have heralded significant advances, both theoretical and applied, in this direction, thus getting us one step closer to realizing such goals"-- 24 cm Date : 2011 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (2),

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Introduction to color imaging science / par Lee,, Hsien-Che. Publication : [S.l.] : Cambridge University Press, 2005 . 716 p. ; , Colour imaging technology has become almost ubiquitous in modern life in the form of monitors, liquid crystal screens, colour printers, scanners, and digital cameras. This book is a comprehensive guide to the scientific and engineering principles of colour imaging. It covers the physics of light and colour, how the eye and physical devices capture colour images, how colour is measured and calibrated, and how images are processed. It stresses physical principles and includes a wealth of real-world examples. The book will be of value to scientists and engineers in the colour imaging industry and, with homework problems, can also be used as a text for graduate courses on colour imaging. 25 cm. Date : 2005 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Statistical methods in counterterrorism : game theory, modeling, syndromic surveillance, and biometric authentication.   Publication : [S.l.] : Springer, 2006 . 292 p. ; , All the data was out there to warn us of this impending attack, why didn't we see it?" This was a frequently asked question in the weeks and months after the terrorist attacks on the World Trade Center and the Pentagon on September 11, 2001. In the wake of the attacks, statisticians moved quickly to become part of the national response to the global war on terror. This book is an overview of the emerging research program at the intersection of national security and statistical sciences. A wide range of talented researchers address issues in - Syndromic Surveillance---How do we detect and recognize bioterrorist events? - Modeling and Simulation---How do we better understand and explain complex processes so that decision makers can take the best course of action? - Biometric Authentication---How do we pick the terrorist out of the crowd of faces or better match the passport to the traveler? - Game Theory---How do we understand the rules that terrorists are playing by? This book includes technical treatments of statistical issues that will be of use to quantitative researchers as well as more general examinations of quantitative approaches to counterterrorism that will be accessible to decision makers with stronger policy backgrounds. Dr. Alyson G. Wilson is a statistician and the technical lead for DoD programs in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. Gregory D. Wilson is a rhetorician and ethnographer in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. David H. Olwell is chair of the Department of Systems Engineering at the Naval Postgraduate School in Monterey, California. 24 cm. Date : 2006 Disponibilité : Exemplaires disponibles: La bibliothèque des Sciences Juridiques, Economiques et de Gestion (1),

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Understanding intrusion detection through visualization / par Axelsson, Stefan. Publication : [S.l.] : Springer, 2005 . 145 p. ; , With the ever increasing use of computers for critical systems, computer security that protects data and computer systems from intentional, malicious intervention, continues to attract attention. Among the methods for defense, the application of a tool to help the operator identify ongoing or already perpetrated attacks (intrusion detection), has been the subject of considerable research in the past ten years. A key problem with current intrusion detection systems is the high number of false alarms they produce. Understanding Intrusion Detection through Visualization presents researchïɪħon why false alarms are, and will remain a problem; then appliesïɪħresults from the field of information visualization to the problem of intrusion detection. This approach promises to enable the operator to identify false (and true) alarms, while aiding the operator to identify other operational characteristics of intrusion detection systems. Thisïɪħvolume presents four different visualization approaches, mainly applied to data from web server access logs. 24 cm. Date : 2005 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Symbolic projection for image information retrieval and spatial reasoning / par Chang, S. K. Publication : . 1 online resource (xii, 324 pages, 2 pages of plates) : Disponibilité :  http://www.sciencedirect.com/science/book/9780121680305,

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Introduction to Image Processing Using R Learning by Examples / par Frery, Alejandro C. Publication : . XV, 87 p. 42 illus., 17 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-4950-7,

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Concise Computer Vision An Introduction into Theory and Algorithms / par Klette, Reinhard. Publication : . XVIII, 429 p. 298 illus., 229 illus. in color. Disponibilité :  http://dx.doi.org/10.1007/978-1-4471-6320-6,

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