Download or read book Statistical Pattern Recognition for Breast Cancer Research written by Thomas D. Parsons and published by . This book was released on 2002 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Use of Statistical Pattern Recognition in Diagnostic Ultrasound for the Detection of Breast Cancer written by Kim M. Butler and published by . This book was released on 1996 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Pattern Recognition Methods for Diagnosis of Cancer Using Gene Expression Data written by Elsa Naser and published by . This book was released on 2002 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Statistical Pattern Recognition written by Keinosuke Fukunaga and published by Elsevier. This book was released on 2013-10-22 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Interpretation of Patterns written by Rajat K. De and published by World Scientific. This book was released on 2010 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. Combining information with a Bayesian multi-class multi-kernel pattern recognition machine / T. Damoulas and M.A. Girolami -- 2. Image quality assessment based on weighted perceptual features / D.V. Rao and L.P. Reddy -- 3. Quasi-reversible two-dimension fractional differentiation for image entropy reduction / A. Nakib [und weitere] -- 4. Parallel genetic algorithm based clustering for object and background classification / P. Kanungo, P.K. Nanda and A. Ghosh -- 5. Bipolar fuzzy spatial information : first operations in the mathematical morphology setting / I. Bloch -- 6. Approaches to intelligent information retrieval / G. Pasi -- 7. Retrieval of on-line signatures / H.N. Prakash and D.S. Guru -- 8. A two stage recognition scheme for offline handwritten Devanagari Words / B. Shaw and S.K. Parui -- 9. Fall detection from a video in the presence of multiple persons / V. Vishwakarma, S. Sural and C. Mandal -- 10. Fusion of GIS and SAR statistical features for earthquake damage mapping at the block scale / G. Trianni [und weitere] -- 11. Intelligent surveillance and Pose-invariant 2D face classification / B.C. Lovell, C. Sanderson and T. Shan -- 12. Simple machine learning approaches to safety-related systems / C. Moewes, C. Otte and R. Kruse -- 13. Nonuniform multi level crossings for signal reconstruction / N. Poojary, H. Kumar and A. Rao -- 14. Adaptive web services brokering / K.M. Gupta and D.W. Aha -- 15. Granular support vector machine based method for prediction of solubility of proteins on over expression in Escherichia Coli and breast cancer classification / P. Kumar, B.D. Kulkarni and V.K. Jayaraman
Download or read book Breast Cancer written by Yeoung-Ching Ting and published by . This book was released on 1975 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by Newnes. This book was released on 1999 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an introduction to statistical pattern recognition theory and techniques. Most of the material presented in this book is concerned with discrimination and classification and has been drawn from a wide range of literature including that of engineering, statistics, computer science and the social sciences. This book is an attempt to provide a concise volume containing descriptions of many of the most useful of today's pattern processing techniques including many of the recent advances in nonparametric approaches to discrimination developed in the statistics literature and elsewhere. The techniques are illustrated with examples of real-world applications studies. Pointers are also provided to the diverse literature base where further details on applications, comparative studies and theoretical developments may be obtained"--Page [xv].
Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by John Wiley & Sons. This book was released on 2011-10-13 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: Provides a self-contained introduction to statistical pattern recognition. Includes new material presenting the analysis of complex networks. Introduces readers to methods for Bayesian density estimation. Presents descriptions of new applications in biometrics, security, finance and condition monitoring. Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications Describes mathematically the range of statistical pattern recognition techniques. Presents a variety of exercises including more extensive computer projects. The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields. www.wiley.com/go/statistical_pattern_recognition
Download or read book Pattern Recognition Applied to the Computer aided Detection and Diagnosis of Breast Cancer from Dynamic Contrast enhanced Magnetic Resonance Breast Images written by Jacob Levman and published by . This book was released on 2010 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this research is to improve the breast cancer screening process based on magnetic resonance imaging (MRI). In a typical MRI breast examination, a radiologist is responsible for visually examining the MR images acquired during the examination and identifying suspect tissues for biopsy. It is known that if multiple radiologists independently analyze the same examinations and we biopsy any lesion that any of our radiologists flagged as suspicious then the overall screening process becomes more sensitive but less specific. Unfortunately cost factors prohibit the use of multiple radiologists for the screening of every breast MR examination. It is thought that instead of having a second expert human radiologist to examine each set of images, that the act of second reading of the examination can be performed by a computer-aided detection and diagnosis system. The research presented in this thesis is focused on the development of a computer-aided detection and diagnosis system for breast cancer screening from dynamic contrast-enhanced magnetic resonance imaging examinations. This thesis presents new computational techniques in supervised learning, unsupervised learning and classifier visualization. The techniques have been applied to breast MR lesion data and have been shown to outperform existing methods yielding a computer aided detection and diagnosis system with a sensitivity of 89% and a specificity of 70%.
Download or read book On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling written by Addisson Salazar and published by Springer. This book was released on 2014-08-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.
Download or read book Structural Syntactic and Statistical Pattern Recognition written by Georgy Gimel ́farb and published by Springer. This book was released on 2012-10-22 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2012) and Statistical Techniques in Pattern Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012 as a satellite event of the 21st International Conference on Pattern Recognition, ICPR 2012. The 80 revised full papers presented together with 1 invited paper and the Pierre Devijver award lecture were carefully reviewed and selected from more than 120 initial submissions. The papers are organized in topical sections on structural, syntactical, and statistical pattern recognition, graph and tree methods, randomized methods and image analysis, kernel methods in structural and syntactical pattern recognition, applications of structural and syntactical pattern recognition, clustering, learning, kernel methods in statistical pattern recognition, kernel methods in statistical pattern recognition, as well as applications of structural, syntactical, and statistical methods.
Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by John Wiley & Sons. This book was released on 2003-07-25 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a
Download or read book Pattern Recognition written by Pierre A. Devijver and published by Prentice Hall. This book was released on 1982 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robustness in Statistical Pattern Recognition written by Y. Kharin and published by Springer. This book was released on 2012-12-22 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with important problems of robust (stable) statistical pat tern recognition when hypothetical model assumptions about experimental data are violated (disturbed). Pattern recognition theory is the field of applied mathematics in which prin ciples and methods are constructed for classification and identification of objects, phenomena, processes, situations, and signals, i. e. , of objects that can be specified by a finite set of features, or properties characterizing the objects (Mathematical Encyclopedia (1984)). Two stages in development of the mathematical theory of pattern recognition may be observed. At the first stage, until the middle of the 1970s, pattern recogni tion theory was replenished mainly from adjacent mathematical disciplines: mathe matical statistics, functional analysis, discrete mathematics, and information theory. This development stage is characterized by successful solution of pattern recognition problems of different physical nature, but of the simplest form in the sense of used mathematical models. One of the main approaches to solve pattern recognition problems is the statisti cal approach, which uses stochastic models of feature variables. Under the statistical approach, the first stage of pattern recognition theory development is characterized by the assumption that the probability data model is known exactly or it is esti mated from a representative sample of large size with negligible estimation errors (Das Gupta, 1973, 1977), (Rey, 1978), (Vasiljev, 1983)).
Download or read book Statistical Pattern Recognition written by Jigang Wang and published by . This book was released on 2006 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Pattern Recognition written by Chi-Hau Chen and published by . This book was released on 1973-01-01 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: The text presents a concise, up-to-date treatment of the fundamental concepts and techniques in statistical pattern recognition. It offers broad and balanced views on various approaches that have widespread application not only in designing better recognition machines, but also in such areas as statistical data processing, communication and control systems, and the computer-related fields. Discussions of linear and non-linear classification theories, representation of patterns, and feature selection using information statistics provide a basic understanding of the subject. Parametric and nonparametric methods of recognition with unknown or partially unknown probability density functions are covered in great detail. An alternative approach to pattern recognition is discussed in a chapter devoted to sequential decision making, feature ordering, and the application of learning algorithms to communication theory and systems. (Author).
Download or read book Pattern Recognition Applied to the Computer aided Detection and Diagnosis of Breast Cancer from Dynamic Contrast enhanced Magnetic Resonance Breast Images written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: PhD.
