Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Download The Development Of Neural Networks For Statistical Classification And Regression full books in PDF, epub, and Kindle. Read online The Development Of Neural Networks For Statistical Classification And Regression ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Author : Donald Michie
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 312 pages
Book Rating : 4.:/5 (318 download)
Download or read book Machine Learning, Neural and Statistical Classification written by Donald Michie and published by Prentice Hall. This book was released on 1994 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Osval Antonio Montesinos López
Publisher : Springer Nature
ISBN 13 : 3030890104
Total Pages : 707 pages
Book Rating : 4.0/5 (38 download)
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Author : G David Garson
Publisher : SAGE
ISBN 13 : 0857026275
Total Pages : 201 pages
Book Rating : 4.8/5 (57 download)
Download or read book Neural Networks written by G David Garson and published by SAGE. This book was released on 1998-09-24 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modelling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.
Author : Basilio de Braganca Pereira
Publisher : CRC Press
ISBN 13 : 0429775555
Total Pages : 248 pages
Book Rating : 4.4/5 (297 download)
Download or read book Statistical Learning Using Neural Networks written by Basilio de Braganca Pereira and published by CRC Press. This book was released on 2020-09-01 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
Author : Ke-Lin Du
Publisher : Springer Nature
ISBN 13 : 1447174526
Total Pages : 996 pages
Book Rating : 4.4/5 (471 download)
Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Nature. This book was released on 2019-09-12 with total page 996 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Author : Jgen Schmann
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 424 pages
Book Rating : 4.3/5 (91 download)
Download or read book Pattern Classification written by Jgen Schmann and published by Wiley-Interscience. This book was released on 1996-03-15 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: PATTERN CLASSIFICATION a unified view of statistical and neural approaches The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable. Offering a lucid presentation of complex applications and their algorithms, Pattern Classification is an invaluable resource for researchers, engineers, and graduate students in this rapidly developing field.
Author : Christopher M. Bishop
Publisher : Oxford University Press
ISBN 13 : 0198538642
Total Pages : 501 pages
Book Rating : 4.1/5 (985 download)
Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Author : Alan J. Izenman
Publisher : Springer Science & Business Media
ISBN 13 : 0387781897
Total Pages : 757 pages
Book Rating : 4.3/5 (877 download)
Download or read book Modern Multivariate Statistical Techniques written by Alan J. Izenman and published by Springer Science & Business Media. This book was released on 2009-03-02 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Author : Gérard Dreyfus
Publisher : Springer Science & Business Media
ISBN 13 : 3540288473
Total Pages : 509 pages
Book Rating : 4.5/5 (42 download)
Download or read book Neural Networks written by Gérard Dreyfus and published by Springer Science & Business Media. This book was released on 2005-11-25 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Author : I.K. Sethi
Publisher : Elsevier
ISBN 13 : 148329787X
Total Pages : 286 pages
Book Rating : 4.4/5 (832 download)
Download or read book Artificial Neural Networks and Statistical Pattern Recognition written by I.K. Sethi and published by Elsevier. This book was released on 2014-06-28 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.
Author : Norman Matloff
Publisher : CRC Press
ISBN 13 : 1351645897
Total Pages : 439 pages
Book Rating : 4.3/5 (516 download)
Download or read book Statistical Regression and Classification written by Norman Matloff and published by CRC Press. This book was released on 2017-09-19 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
Author : Trevor Hastie
Publisher : Springer Science & Business Media
ISBN 13 : 0387216065
Total Pages : 545 pages
Book Rating : 4.3/5 (872 download)
Download or read book The Elements of Statistical Learning written by Trevor Hastie and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : J L Jensen
Publisher : CRC Press
ISBN 13 : 9780412465307
Total Pages : 324 pages
Book Rating : 4.4/5 (653 download)
Download or read book Networks and Chaos - Statistical and Probabilistic Aspects written by J L Jensen and published by CRC Press. This book was released on 1993-07-22 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume consists of a collection of tutorial papers by leading experts on statistical and probabilistic aspects of chaos and networks, in particular neural networks. While written for the non-expert, they are intended to bring the reader up to the forefront of knowledge and research in the subject areas concerned. The papers, which contain extensive references to the literature, can separately or in various combinations serve as bases for short- or full-length courses, at graduate or more advanced levels. The papers are directed not only to mathematical statisticians but also to students and researchers in related fields of biology, engineering, geology, physics and probability.
Author : Hariom Tatsat
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492073008
Total Pages : 426 pages
Book Rating : 4.4/5 (92 download)
Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Author : John Anderson
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (632 download)
Download or read book Proceedings of the international conference on Machine Learning written by John Anderson and published by . This book was released on 19?? with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Christopher M. Bishop
Publisher : Springer
ISBN 13 : 9781493938438
Total Pages : 0 pages
Book Rating : 4.9/5 (384 download)
Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. 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.
Author : Sandhya Samarasinghe
Publisher : CRC Press
ISBN 13 : 1420013068
Total Pages : 596 pages
Book Rating : 4.4/5 (2 download)
Download or read book Neural Networks for Applied Sciences and Engineering written by Sandhya Samarasinghe and published by CRC Press. This book was released on 2016-04-19 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in
