Pattern Analysis
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Author | : Martin Fowler |
Publisher | : Addison-Wesley Professional |
Total Pages | : 398 |
Release | : 1997 |
Genre | : Object-oriented methods (Computer science). |
ISBN | : 9780201895421 |
Martin Fowler is a consultant specializing in object-oriented analysis and design. This book presents and discusses a number of object models derived from various problem domains. All patterns and models presented have been derived from the author's own consulting work and are based on real business cases.
Author | : H. Niemann |
Publisher | : Springer Science & Business Media |
Total Pages | : 316 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 3642966500 |
This book is devoted to pattern analysis, that is, the automatic construc tion of a symbolic description for a complex pattern, like an image or con nected speech. Pattern analysis thus tries to simulate certain capabilities which go without saying in any human central nervous system. The increasing interest and growing efforts at solving the problems related with pattern analysis are motivated by the challenge of the problem and the expected ap plications. Potential applications are numerous and result from the fact that data can be gathered and stored by modern devices in ever increasing extent, thus making the finding of particular interesting facts or events in these hosts of data an ever increasing problem. It was tried to organize the book around one particular view of pattern analysis: the view that pattern analysis requires an appropriate set of modules operating on a common data base which contains interme processing diate results of processing. Although other views are certainly possible, this one was adopted because the author feels that it is a useful idea, be cause the size of this book had to be kept within reasonable bounds, and because it facilitated the composition of fairly self-contained chapters.
Author | : John Shawe-Taylor |
Publisher | : Cambridge University Press |
Total Pages | : 520 |
Release | : 2004-06-28 |
Genre | : Computers |
ISBN | : 9780521813976 |
Author | : Barry N. Boots |
Publisher | : SAGE Publications, Incorporated |
Total Pages | : 104 |
Release | : 1988-03 |
Genre | : Mathematics |
ISBN | : |
Boots and Getis provide a concise explanation of point pattern analysis - a series of techniques for identifying patterns of clustering or regularity in a set of geographical locations. They discuss quadrat and distance methods of measurement, and consider the problems associated with these methods. The authors also outline and compare other measures of arrangement, suggesting when these techniques should be used.
Author | : Tom Bevel |
Publisher | : CRC Press |
Total Pages | : 420 |
Release | : 2001-09-26 |
Genre | : Law |
ISBN | : 1420041258 |
Bloodstain pattern analysis helps establish events associated with violent crimes. It is a critical bridge between forensics and the definition of a precise crime reconstruction. The second edition of this bestselling book is thoroughly updated to employ recent protocols, including the application of scientific method, the use of flow charts, and the inter-relationship of crime scene analysis to criminal profiling. It provides more illustrations, including color photographs, and explains the use of computer programs to create demonstrative evidence for court.
Author | : Stuart H. James |
Publisher | : CRC Press |
Total Pages | : 574 |
Release | : 2005-05-26 |
Genre | : Law |
ISBN | : 1420039466 |
As witnessed in landmark criminal cases, the quality and integrity of bloodstain evidence can be a crucial factor in determining a verdict.
Author | : David Mumford |
Publisher | : CRC Press |
Total Pages | : 422 |
Release | : 2010-08-09 |
Genre | : Computers |
ISBN | : 1439865566 |
Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis o
Author | : Marcello Pelillo |
Publisher | : Springer Science & Business Media |
Total Pages | : 293 |
Release | : 2013-11-26 |
Genre | : Computers |
ISBN | : 1447156285 |
This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification algorithms; reviews similarity measures for non-vectorial data, considering both a “kernel tailoring” approach and a strategy for learning similarities directly from training data; describes various methods for “structure-preserving” embeddings of structured data; formulates classical pattern recognition problems from a purely game-theoretic perspective; examines two large-scale biomedical imaging applications.
Author | : Sherif M. Yacoub |
Publisher | : Addison-Wesley Professional |
Total Pages | : 416 |
Release | : 2004 |
Genre | : Computers |
ISBN | : 9780201776409 |
- Exploit the significant power of design patterns and make better design decisions with the proven POAD methodology - Improve software quality and reliability while reducing costs and maintenance efforts - Practical case studies and illustrative examples help the reader manage the complexity of software development
Author | : Yun Fu |
Publisher | : Springer Science & Business Media |
Total Pages | : 264 |
Release | : 2012-11-19 |
Genre | : Technology & Engineering |
ISBN | : 1461444578 |
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.