This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Presents current trends and potential future developments byleading researchers in immersive media production, delivery,rendering and interaction The underlying audio and video processing technology that isdiscussed in the book relates to areas such as 3D objectextraction, audio event detection; 3D sound rendering and facedetection, gesture analysis and tracking using video and depthinformation. The book will give an insight into current trends anddevelopments of future media production, delivery and reproduction.Consideration of the complete production, processing anddistribution chain will allow for a full picture to be presented tothe reader. Production developments covered will include integratedworkflows developed by researchers and industry practitioners aswell as capture of ultra-high resolution panoramic video and 3Dobject based audio across a range of programme genres. Distributiondevelopments will include script based format agnostic networkdelivery to a full range of devices from large scale publicpanoramic displays with wavefield synthesis and ambisonic audioreproduction to ’small screen’ mobile devices. Keydevelopments at the consumer end of the chain apply to both passiveand interactive viewing modes and will incorporate user interfacessuch as gesture recognition and ‘second screen’ devicesto allow manipulation of the audio visual content. Presents current trends and potential future developments byleading researchers in immersive media production, delivery,rendering and interaction. Considers the complete production, processing and distributionchain illustrating the dependencies and the relationship betweendifferent components. Proposes that a format-agnostic approach to the production anddelivery of broadcast programmes will overcome the problems facedwith the steadily growing number of production and deliveryformats. Explains the fundamentals of media production in addition tothe complete production chain, beyond current-state-of-the-artthrough to presenting novel approaches and technologies for futuremedia production. Focuses on the technologies that will allow for the realizationof an E2E media platform that supports flexible contentrepresentations and interactivity for users. An essential read for Researchers and developers of audio-visualtechnology in industry and academia, such as engineers in broadcasttechnology companies and students working toward a career in therapidly changing area of broadcast both from a production and anengineering perspective.
Applicable to any problem that requires a finite number of solutions, finite state-based models (also called finite state machines or finite state automata) have found wide use in various areas of computer science and engineering. Handbook of Finite State Based Models and Applications provides a complete collection of introductory materials on finite state theories, algorithms, and the latest domain applications. For beginners, the book is a handy reference for quickly looking up model details. For more experienced researchers, it is suitable as a source of in-depth study in this area. The book first introduces the fundamentals of automata theory, including regular expressions, as well as widely used automata, such as transducers, tree automata, quantum automata, and timed automata. It then presents algorithms for the minimization and incremental construction of finite automata and describes Esterel, an automata-based synchronous programming language for embedded system software development. Moving on to applications, the book explores regular path queries on graph-structured data, timed automata in model checking security protocols, pattern matching, compiler design, and XML processing. It also covers other finite state-based modeling approaches and applications, including Petri nets, statecharts, temporal logic, and UML state machine diagrams.
The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.
This volume contains revised and extended research articles written by prominent researchers participating in the ICF4C 2011 conference. 2011 International Conference on Future Communication, Computing, Control and Management (ICF4C 2011) has been held on December 16-17, 2011, Phuket, Thailand. Topics covered include intelligent computing, network management, wireless networks, telecommunication, power engineering, control engineering, Signal and Image Processing, Machine Learning, Control Systems and Applications, The book will offer the states of arts of tremendous advances in Computing, Communication, Control, and Management and also serve as an excellent reference work for researchers and graduate students working on Computing, Communication, Control, and Management Research.
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.
"This 10-volume compilation of authoritative, research-based articles contributed by thousands of researchers and experts from all over the world emphasized modern issues and the presentation of potential opportunities, prospective solutions, and future directions in the field of information science and technology"--Provided by publisher.
institute's motto "Unity in Diversity." As evidence and justi'cation of the int- disciplinary research comprising statistics and computer science, one may note thatstatistics providesone ofthe bestparadigmsfor learning,andit hasbecome an integralpart of the theories/paradigmsof machine learning, e.g., arti'cial - telligence, neural networks, brain mapping, data mining, and search machines on the Internet. Zadeh, the founder of fuzzy set theory, has observed that there are three essential ingredients for dramatic success in computer applications, namely, a fuzzy model of data, Bayesian inference and genetic algorithms for optimization. Similarly, statistical science will be a part, in many ways, of the validation of the tentative model of the human brain, its functions and prop- ties, including consciousness. As a mark of the signi'cant achievements in these activities in ISI, special mention may be made of the DOE-sponsored KBCS Nodal Center of ISI in the 1980s and the Center for Soft Computing Research of ISI recently established in 2004 by the DST, Government of India. The soft computing center is the ?rst national initiative in the country in this domain, and has many imp- tant objectives like providing a six-month value addition certi'cate course for post-graduates, enriching national institutes, e.g., NITs through funding for - search in soft computing, establishing linkage to premier institutes/industries, organizing specialized courses, apart from conducting fundamental research.
Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.