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Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.
Exploring Hong Kong presents a vivid and multidimensional portrait of Hong Kong, one of Asia's most exciting cities. Inspired by his 20-year love affair with Hong Kong, Steven K. Bailey has transformed the typical Hong Kong guidebook by dispensing with the usual laundry lists of sights, hotels, and restaurants. In their place are thoughtfully written chapters that offer the author's personal perspective on how to best explore Hong Kong. From dolphin watches and back-country hikes to street markets, temples, and ferry rides, Exploring Hong Kong contains 40 richly detailed experiences that will unite travelers with the soul of one of the most dynamic cities in Asia. Book jacket.
The field of machine learning is gaining a lot of attention around the world, both in the research community and in the business world. Learning by machine is becoming increasingly important in many aspects of modern life. Deep learning neural networks have been responsible for several recent technological advances, including those in the fields of computer vision, voice processing, machine translation, and reinforcement learning. As a direct consequence of this, neural networks have developed into an indispensable instrument in the toolset of every data scientist. This book explains neural networks, including what they are, why they are effective algorithms and why they have the structure t...
This book constitutes the refereed proceedings of the Third International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006, held in federation with the Second International Conference on Natural Computation ICNC 2006. The book presents 115 revised full papers and 50 revised short papers. Coverage includes neural computation, quantum computation, evolutionary computation, DNA computation, fuzzy computation, granular computation, artificial life, innovative applications to knowledge discovery, finance, operations research, and more.
Document image analysis is the automatic computer interpretation of images of printed and handwritten documents, including text, drawings, maps, music scores, etc. Research in this field supports a rapidly growing international industry. This is the first book to offer a broad selection of state-of-the-art research papers, including authoritative critical surveys of the literature, and parallel studies of the architectureof complete high-performance printed-document reading systems. A unique feature is the extended section on music notation, an ideal vehicle for international sharing of basic research. Also, the collection includes important new work on line drawings, handwriting, character and symbol recognition, and basic methodological issues. The IAPR 1990 Workshop on Syntactic and Structural Pattern Recognition is summarized,including the reports of its expert working groups, whose debates provide a fascinating perspective on the field. The book is an excellent text for a first-year graduate seminar in document image analysis,and is likely to remain a standard reference in the field for years.
The four-volume set comprising LNCS volumes 3951/3952/3953/3954 constitutes the refereed proceedings of the 9th European Conference on Computer Vision, ECCV 2006, held in Graz, Austria, in May 2006. The 192 revised papers presented were carefully reviewed and selected from a total of 811 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, statistical models and visual learning, 3D reconstruction and multi-view geometry, energy minimization, tracking and motion, segmentation, shape from X, visual tracking, face detection and recognition, illumination and reflectance modeling, and low-level vision, segmentation and grouping.
This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing. The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study.
This book constitutes the refereed proceedings of the First International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2004, held in Barcelona, Spain in August 2004. The 26 revised full papers presented together with 4 invited papers were carefully reviewed and selected from 53 submissions. The papers are devoted to topics like models for information fusion, aggregation operators, model selection, fuzzy integrals, fuzzy sets, fuzzy multisets, neural learning, rule-based classification systems, fuzzy association rules, algorithmic learning, diagnosis, text categorization, unsupervised aggregation, the Choquet integral, group decision making, preference relations, vague knowledge processing, etc.
This book constitutes the refereed proceedings of the 5th International Workshop on Document Analysis Systems, DAS 2002, held in Princeton, NJ, USA in August 2002 with sponsorship from IAPR.The 44 revised full papers presented together with 14 short papers were carefuly reviwed and selected for inclusion in the book. All current issues in document analysis systems are adressed. The papers are organized in topical sections on OCR features and systems, handwriting recognition, layout analysis, classifiers and learning, tables and forms, text extraction, indexing and retrieval, document engineering, and new applications.
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, ex...