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.
Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Brandenburg University of Technology, Cottbus, March 12-14, 2003
Author: Daniel Baier
Publisher: Springer Science & Business Media
Category: Language Arts & Disciplines
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.
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.
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site
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.
Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.
6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005, Proceedings
Author: Nikunj C. Oza
Publisher: Springer Science & Business Media
This book constitutes the refereed proceedings of the 6th International Workshop on Multiple Classifier Systems, MCS 2005, held in Seaside, CA, USA in June 2005. The 42 revised full papers presented were carefully reviewed and are organized in topical sections on boosting, combination methods, design of ensembles, performance analysis, and applications. They exemplify significant advances in the theory, algorithms, and applications of multiple classifier systems – bringing the different scientific communities together.
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.
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
"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.