[PDF] Bayesian Learning In Classification And Density Estimation eBook

Author : Andrew R. Webb
Publisher : John Wiley & Sons
Page : 604 pages
File Size : 10,67 MB
Release : 2011-10-13
Category : Mathematics
ISBN : 1119961408

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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

Author : Masashi Sugiyama
Publisher : Cambridge University Press
Page : 343 pages
File Size : 32,65 MB
Release : 2012-02-20
Category : Computers
ISBN : 0521190177

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This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.

Author : Scott Davies
Publisher :
Page : 181 pages
File Size : 10,26 MB
Release : 2002
Category : Data compression (Computer science)
ISBN :

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Abstract: "Many important data analysis tasks can be addressed by formulating them as probability estimation problems. For example, a popular general approach to automatic classification problems is to learn a probabilistic model of each class from data in which the classes are known, and then use Bayes's rule with these models to predict the correct classes of other data for which they are not known. Anomaly detection and scientific discovery tasks can often be addressed by learning probability models over possible events and then looking for events to which these models assign low probabilities. Many data compression algorithms such as Huffman coding and arithmetic coding rely on probabilistic models of the data stream in order [sic] achieve high compression rates. In this thesis we examine several aspects of probability estimation algorithms. In particular, we focus on the automatic learning and use of probability models based on Bayesian networks, a convenient formalism in which the probability estimation task is split into many simpler subtasks. We also emphasize computational efficiency. First, we provide Bayesian network-based algorithms for losslessly compressing large discrete datasets. We show that these algorithms can produce compression ratios dramatically higher than those achieved by popular compression programs such as gzip or bzip2, yet still maintain megabyte-per-second decoding speeds on well-aged conventional PCs. Next, we provide algorithms for quickly learning Bayesian network-based probability models over domains with both discrete and continuous variables. We show how recently developed methods for quickly learning Gaussian mixture models from data [Moo99] can be used to learn Bayesian networks modeling complex nonlinear relationships over dozens of variables from thousands of datapoints in a practical amount of time. Finally we explore a large space of tree-based density learning algorithms, and show that they can be used to quickly learn Bayesian networks that can provide accurate density estimates and that are fast to evaluate."

Author : Antonino Freno
Publisher : Springer Science & Business Media
Page : 217 pages
File Size : 28,84 MB
Release : 2011-04-11
Category : Technology & Engineering
ISBN : 3642203086

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This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.

Author : David G. T. Denison
Publisher : John Wiley & Sons
Page : 302 pages
File Size : 13,53 MB
Release : 2002-05-06
Category : Mathematics
ISBN : 9780471490364

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Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.

Author : Karl-Rudolf Koch
Publisher : Springer
Page : 205 pages
File Size : 11,26 MB
Release : 2006-04-11
Category : Science
ISBN : 3540466010

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This introduction to Bayesian inference places special emphasis on applications. All basic concepts are presented: Bayes' theorem, prior density functions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinear model, mixed model and in the model with unknown variance and covariance components is considered. Solutions are supplied for the classification, for the posterior analysis based on distributions of robust maximum likelihood type estimates, and for the reconstruction of digital images.