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Computer manual in MATLAB to accompany Pattern classification / David G Stork
Title : Computer manual in MATLAB to accompany Pattern classification Material Type: printed text Authors: David G Stork, Author ; Elad Yom-Tov, Author Edition statement: 2nd edition Publisher: Hoboken, N.J. : J. Wiley & Sons Publication Date: 2004 Pagination: ix, 134 p. Layout: ill. Size: 28 cm ISBN (or other code): 978-0-471-42977-7 General note: Includes bibliographical references (p. 127-128)
Includes index (p. 129- 134)Languages : English (eng) Original Language : English (eng) Descriptors: MATLAB.
Pattern recognition systems - Data processing
Statistical decision --Data processing.Class number: 006.4 Abstract: "Computer Manual to Accompany Pattern Classification"- is an excellent companion to Duda : Pattern Classification, 2nd ed,(DH&S). The code contains all algorithms described in Duda as well as supporting algorithms for data generation and visualization. The Manual uses the same terminology as the DH&S text and contains step-by-step worked examples, including many of the examples and figures in the textbook. The Manual is accompanied by software that is available electronically. The software contains all algorithms in DH&S, indexed to the textbook, and uses symbols and notation as close as possible to the textbook. The code is self-annotating so the user can easily navigate, understand and modify the code. Contents note: Introduction to MATLAB; Basic navigation and interaction; Scalars, Variables and basic arithmetic; Relational and logical operators; Lists, Vectors and matrices; Matrix multiplication; Vector and matrix Norms; Determinants, Inverses and pseudoinverses; Matrix Powers and exponentials; Eigenvalues and eigenvectors; Dataa analysis; Clearing variables and functions; Data types; Programming in MATLAB; Scripts; Functions; Flow control; User input; Debugging; Data, and File input and output; Strings; Operations on strings; Classification toolbox; Loading the toolbox and starting MATLAB; Graphical user interface; Introductory examples; GUI controls; Creating your own data files; Classifying using the text-based interface; Classifier comparisons; How to add new algorithms; Adding a new feature selection algorithm; List of functions; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=13881 Hold
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Barcode Call number Media type Location Section Status 1702-000701 006.4 Sto-Com 2004 General Collection SEEU Library Skopje English Available Feature extraction & image processing for computer vision / Mark S. Nixon
Title : Feature extraction & image processing for computer vision Material Type: printed text Authors: Mark S. Nixon, Author ; Alberto S. Aguado, Author Publisher: Elsevier/Academic Press, (Amsterdam -London) Publication Date: 2012 Pagination: xvii, 607 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-0-12-396549-3 General note: Includes bibliographical references
Includes index (p. 601-607)Languages : English (eng) Original Language : English (eng) Descriptors: Computer graphics
Computer vision
Image processing - Digital techniques
Pattern recognition systems - Data processingClass number: 006.6 Abstract: This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Ada boost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. Contents note: Images, Sampling, and Frequency Domain Processing; Basic Image Processing Operations; Low-Level Feature Extraction (including edge detection); High-Level Feature Extraction: Fixed Shape Matching; High-Level Feature Extraction : Deform able Shape Analysis; Object Description; Introduction to Texture Description, Segmentation, and Classification; Moving Object Detection and and Description; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=16230 Hold
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Barcode Call number Media type Location Section Status 1702-001534 006.6 Nix-Fea 2012 General Collection SEEU Library Skopje English Available Pattern recognition / Sergios Theodoridis
Title : Pattern recognition Material Type: printed text Authors: Sergios Theodoridis, Author ; Konstantinos Koutroumbas, Author Edition statement: 4th ed. Publisher: Elsevier/Academic Press, (Amsterdam -London) Publication Date: 2009 Pagination: xvii, 961 p. Layout: ill. Size: 25 cm ISBN (or other code): 978-1-597-49272-0 General note: Includes bibliographical references and index Languages : English (eng) Original Language : English (eng) Descriptors: Pattern recognition systems - Data processing Class number: 006.4 Abstract: "Pattern recognition" - this book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition : semi-supervised learning, combining clustering algorithms, and relevance feedback. Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques. Many more diagrams included--now in two color--to provide greater insight through visual presentation· Matlab code of the most common methods are given at the end of each chapter. More Matlab code is available, together with an accompanying manual, via this site. Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. Contents note: Introduction; Classifiers based on bayes decision; Linear classifiers; Nonlinear classifiers; Feature selection; Feature generation I : Data transformation and dimensionality reduction; Feature generation II; Template matching; Context depedant Clarification; System evaluation; Clustering : Basic concepts; Clustering algorithms : algorithms L sequential; Clustering algorithms II : Hierarchical; Clustering algorithms III : Based on function optimization; Clustering algorithms IV : Clustering; Cluster validity; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=13887 Copies
Barcode Call number Media type Location Section Status 1702-000703 006.4 The-Pat 2009 General Collection Library "Max van der Stoel" English Not for loan Pattern recognition and machine learning / Christopher M. Bishop
Title : Pattern recognition and machine learning Material Type: printed text Authors: Christopher M. Bishop, Author Publisher: New York : Springer Publication Date: 2006 Pagination: xx; 738 p. Size: 25 cm ISBN (or other code): 978-0-387-31073-2 General note: Includes appendix (p. 677-710)
Includes bibliographical references (p. 711-728)
Includes index (p. 729-738)Languages : English (eng) Original Language : English (eng) Descriptors: Machine learning
Pattern recognition systems - Data processingClass number: 006.4 Abstract: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. 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. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Contents note: Introduction; Probability distributions; Linear models of regression; Linear models for classification; Neural networks; Kernel methods; Sparse kernel machines; Graphical models; Mixture models and EM; Approximate inference; Sampling methods; Continuous latent variables; Sequential data; Combining models; Data sets; Probability distributions; Properties of matrices; Calculus of variations; Lagrange multipliers; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=16042 Hold
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Barcode Call number Media type Location Section Status 1702-001581 006.4 Bis-Pat 2006 General Collection SEEU Library Skopje English Available