introduction to statistical pattern recognition computer science scientific computing

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Introduction To Statistical Pattern Recognition

Author : Keinosuke Fukunaga
ISBN : 0080478654
Genre : Computers
File Size : 50. 33 MB
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This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

Pattern Recognition

Author : Bernd Radig
ISBN : 9783540454045
Genre : Computers
File Size : 46. 88 MB
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Sometimes milestones in the evolution of the DAGM Symposium become immediately visible. The Technical Committee decided to publish the symposium proceedings completely in English. As a consequence we successfully negotiated with Springer Verlag to publish in the international well accepted series “Lecture Notes in Computer Science”. The quality of the contributions convinced the editors and the lectors. Thanks to them and to the authors. We received 105 acceptable, good, and even excellent manuscripts. We selected carefully, using three reviewers for each anonymized paper, 58 talks and posters. Our 41 reviewers had a hard job evaluating and especially rejecting contributions. We are grateful for the time and effort they spent in this task. The program committee awarded prizes to the best papers. We are much obliged to the generous sponsors. We had three invited talks from outstanding colleagues, namely Bernhard Nebel (Robot Soccer – A Challenge for Cooperative Action and Perception), Thomas Lengauer (Computational Biology – An Interdisciplinary Challenge for Computational Pattern Recognition), and Nassir Navab (Medical and Industrial Augmented Reality: Challenges for Real Time Vision, Computer Graphics, and Mobile Computing). N. Navab even wrote a special paper for this conference, which is included in the proceedings. We were proud that we could convince well known experts to offer tutorials to our participants: H. P. Seidel, Univ. Saarbrücken – A Framework for the Acquisition, Processing, and Interactive Display of High Quality 3D Models; S. Heuel, Univ. Bonn – Projective Geometry for Grouping and Orientation Tasks; G. Rigoll, Univ.

Pattern Recognition

Author : DAGM (Organization). Symposium
ISBN : 3540425969
Genre : Computers
File Size : 80. 83 MB
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This book constitutes the refereed proceedings of the 23rd Symposium of the German Association for Pattern Recognition, DAGM 2001, held in Munich, Germany in September 2001. The 58 revised full papers and posters presented were carefully reviewed and selected from a total of 105 submissions. The book offers topical sections on image analysis, 3D-gathering and visualization, image processing, image sequence analysis, classification, active vision, 3D-reconstruction, and interaction of virtual and real worlds.

Pattern Recognition And Machine Intelligence

Author : Santanu Chaudhury
ISBN : 9783642111631
Genre : Computers
File Size : 73. 50 MB
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This book constitutes the refereed proceedings of the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009, held in New Delhi, India in December 2009. The 98 revised papers presented were carefully reviewed and selected from 221 initial submissions. The papers are organized in topical sections on pattern recognition and machine learning, soft computing andapplications, bio and chemo informatics, text and data mining, image analysis, document image processing, watermarking and steganography, biometrics, image and video retrieval, speech and audio processing, as well as on applications.

Advances In Computational Intelligence

Author : Ignacio Rojas
ISBN : 9783319591476
Genre : Computers
File Size : 35. 20 MB
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This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, held in Cadiz, Spain, in June 2017. The 126 revised full papers presented in this double volume were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on Bio-inspired Computing; E-Health and Computational Biology; Human Computer Interaction; Image and Signal Processing; Mathematics for Neural Networks; Self-organizing Networks; Spiking Neurons; Artificial Neural Networks in Industry ANNI'17; Computational Intelligence Tools and Techniques for Biomedical Applications; Assistive Rehabilitation Technology; Computational Intelligence Methods for Time Series; Machine Learning Applied to Vision and Robotics; Human Activity Recognition for Health and Well-Being Applications; Software Testing and Intelligent Systems; Real World Applications of BCI Systems; Machine Learning in Imbalanced Domains; Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles; End-User Development for Social Robotics; Artificial Intelligence and Games; and Supervised, Non-Supervised, Reinforcement and Statistical Algorithms.

Applications Of Soft Computing

Author : Bruno Bosacchi
ISBN : UOM:39015042165491
Genre : Computers
File Size : 82. 4 MB
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Bibliographic Guide To Computer Science

Author :
ISBN : STANFORD:36105013125054
Genre : Computer science
File Size : 62. 44 MB
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Program Of The Twelfth Annual Conference Of The Cognitive Science Society 25 28 July 1990 Cambridge Massachusetts

Author : Cognitive Science Society (U.S.). Conference
ISBN : 0805809384
Genre : Psychology
File Size : 61. 11 MB
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First published in 1990. Routledge is an imprint of Taylor & Francis, an informa company.

Introduction To Statistical Machine Learning

Author : Masashi Sugiyama
ISBN : 9780128023501
Genre : Computers
File Size : 56. 65 MB
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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.

Introduction To Pattern Recognition

Author : Menahem Friedman
ISBN : 9789813105188
Genre : Computers
File Size : 50. 87 MB
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This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.

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