neural networks and statistical learning

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Neural Networks And Statistical Learning

Author : Ke-Lin Du
ISBN : 9781447155713
Genre : Computers
File Size : 38. 87 MB
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Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Statistical Learning With Artificial Neural Network Applied To Health And Environmental Data

Author : Taysseer Sharaf
ISBN : OCLC:927401269
Genre : Artificial intelligence
File Size : 29. 32 MB
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The current study illustrates the utilization of artificial neural network in statistical methodology. More specifically in survival analysis and time series analysis, where both holds an important and wide use in many applications in our real life. We start our discussion by utilizing artificial neural network in survival analysis. In literature there exist two important methodology of utilizing artificial neural network in survival analysis based on discrete survival time method. We illustrate the idea of discrete survival time method and show how one can estimate the discrete model using artificial neural network. We present a comparison between the two methodology and update one of them to estimate survival time of competing risks. To fit a model using artificial neural network, you need to take care of two parts; first one is the neural network architecture and second part is the learning algorithm. Usually neural networks are trained using a non-linear optimization algorithm such as quasi Newton Raphson algorithm. Other learning algorithms are base on Bayesian inference. In this study we present a new learning technique by using a mixture of the two available methodologies for using Bayesian inference in training of neural networks. We have performed our analysis using real world data. We have used patients diagnosed with skin cancer in the United states from SEER database, under the supervision of the National Cancer Institute The second part of this dissertation presents the utilization of artificial neural to time series analysis. We present a new method of training recurrent artificial neural network with Hybrid Monte Carlo Sampling and compare our findings with the popular auto-regressive integrated moving average (ARIMA) model. We used the carbon dioxide monthly average emission to apply our comparison, data collected from NOAA.

The Elements Of Statistical Learning

Author : Trevor Hastie
ISBN : 9780387216065
Genre : Mathematics
File Size : 73. 1 MB
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Neural Networks For Statistical Modeling

Author : Murray Smith
ISBN : STANFORD:36105017638508
Genre : Computers
File Size : 23. 78 MB
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Machine Learning For Spatial Environmental Data

Author : Mikhail Kanevski
ISBN : 0849382378
Genre : Business & Economics
File Size : 75. 57 MB
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Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.

Pattern Recognition And Neural Networks

Author : Brian D. Ripley
ISBN : 0521717701
Genre : Computers
File Size : 83. 3 MB
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Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.

Bayesian Nonparametrics Via Neural Networks

Author : Herbert K. H. Lee
ISBN : 9780898715637
Genre : Mathematics
File Size : 30. 88 MB
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This is the first book to discuss neural networks in a nonparametric regression and classification context, within the Bayesian paradigm.

A Computational Approach To Statistical Learning

Author : Taylor Arnold
ISBN : 9781351694759
Genre : Business & Economics
File Size : 24. 5 MB
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A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

Learning From Data

Author : Vladimir Cherkassky
ISBN : 0470140518
Genre : Computers
File Size : 89. 73 MB
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An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Algebraic Geometry And Statistical Learning Theory

Author : Sumio Watanabe
ISBN : 9780521864671
Genre : Computers
File Size : 57. 63 MB
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Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.

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