Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Download Regression And The Moore Penrose Pseudoinverse full books in PDF, epub, and Kindle. Read online Regression And The Moore Penrose Pseudoinverse ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Author : Albert
Publisher : Academic Press
ISBN 13 : 0080956033
Total Pages : 196 pages
Book Rating : 4.0/5 (89 download)
Download or read book Regression and the Moore-Penrose Pseudoinverse written by Albert and published by Academic Press. This book was released on 1972-12-06 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression and the Moore-Penrose Pseudoinverse
Author : Per Christian Hansen
Publisher : SIAM
ISBN 13 : 089871883X
Total Pages : 220 pages
Book Rating : 4.8/5 (987 download)
Download or read book Discrete Inverse Problems written by Per Christian Hansen and published by SIAM. This book was released on 2010-01-01 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives an introduction to the practical treatment of inverse problems by means of numerical methods, with a focus on basic mathematical and computational aspects. To solve inverse problems, we demonstrate that insight about them goes hand in hand with algorithms.
Author : Adi Ben-Israel
Publisher : Springer Science & Business Media
ISBN 13 : 0387216340
Total Pages : 433 pages
Book Rating : 4.3/5 (872 download)
Download or read book Generalized Inverses written by Adi Ben-Israel and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition accounts for many major developments in generalized inverses while maintaining the informal and leisurely style of the 1974 first edition. Added material includes a chapter on applications, new exercises, and an appendix on the work of E.H. Moore.
Author : Johannes Lederer
Publisher : Springer Nature
ISBN 13 : 3030737926
Total Pages : 355 pages
Book Rating : 4.0/5 (37 download)
Download or read book Fundamentals of High-Dimensional Statistics written by Johannes Lederer and published by Springer Nature. This book was released on 2021-11-16 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.
Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 149203259X
Total Pages : 851 pages
Book Rating : 4.4/5 (92 download)
Download or read book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Author : James W. Demmel
Publisher : SIAM
ISBN 13 : 0898713897
Total Pages : 426 pages
Book Rating : 4.8/5 (987 download)
Download or read book Applied Numerical Linear Algebra written by James W. Demmel and published by SIAM. This book was released on 1997-08-01 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive textbook is designed for first-year graduate students from a variety of engineering and scientific disciplines.
Author : Mariette Awad
Publisher : Apress
ISBN 13 : 1430259906
Total Pages : 263 pages
Book Rating : 4.4/5 (32 download)
Download or read book Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
Author : Calyampudi Radhakrishna Rao
Publisher : John Wiley & Sons
ISBN 13 :
Total Pages : 264 pages
Book Rating : 4.:/5 (319 download)
Download or read book Generalized Inverse of Matrices and Its Applications written by Calyampudi Radhakrishna Rao and published by John Wiley & Sons. This book was released on 1971 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Notations and preliminaries; Generalized inverse of a matrix; Three basic types of g-inverses; Other special types of g-inverse; Projectors, idempotent matrices and partial isometry; Simulatneous reduction of a pair of herminitian forms; Estimation of parameters in linear models; Conditions for optimality and validity of least-squares theory; Distribution of quadratic forms; Miscellaneous applications of g-inverses; Computational methods; Bibliography on generalized inverses and applications; Index.
Author : Alan J. Laub
Publisher : SIAM
ISBN 13 : 0898715768
Total Pages : 159 pages
Book Rating : 4.8/5 (987 download)
Download or read book Matrix Analysis for Scientists and Engineers written by Alan J. Laub and published by SIAM. This book was released on 2005-01-01 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Prerequisites for using this text are knowledge of calculus and some previous exposure to matrices and linear algebra, including, for example, a basic knowledge of determinants, singularity of matrices, eigenvalues and eigenvectors, and positive definite matrices. There are exercises at the end of each chapter."--BOOK JACKET.
Author : Charu C. Aggarwal
Publisher : Springer Nature
ISBN 13 : 3030403440
Total Pages : 507 pages
Book Rating : 4.0/5 (34 download)
Download or read book Linear Algebra and Optimization for Machine Learning written by Charu C. Aggarwal and published by Springer Nature. This book was released on 2020-05-13 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.
Author : Charles L. Byrne
Publisher : A K Peters/CRC Press
ISBN 13 :
Total Pages : 408 pages
Book Rating : 4.3/5 (97 download)
Download or read book Applied Iterative Methods written by Charles L. Byrne and published by A K Peters/CRC Press. This book was released on 2008 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of essays on iterative algorithms and their uses. It focuses on the mathematics of medical image reconstruction, with emphasis on Fourier inversion. The book discusses the problems and algorithms in the context of operators on finite-dimensional Euclidean space.
Author : Peter van Overschee
Publisher : Springer Science & Business Media
ISBN 13 : 1461304652
Total Pages : 263 pages
Book Rating : 4.4/5 (613 download)
Download or read book Subspace Identification for Linear Systems written by Peter van Overschee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering.
Author : Karl-Rudolf Koch
Publisher : Springer Science & Business Media
ISBN 13 : 3662039761
Total Pages : 344 pages
Book Rating : 4.6/5 (62 download)
Download or read book Parameter Estimation and Hypothesis Testing in Linear Models written by Karl-Rudolf Koch and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: A treatment of estimating unknown parameters, testing hypotheses and estimating confidence intervals in linear models. Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters. A chapter on the robust estimation of parameters and several examples have been added to this second edition. The necessary theorems of vector and matrix algebra and the probability distributions of test statistics are derived so as to make this book self-contained. Geodesy students as well as those in the natural sciences and engineering will find the emphasis on the geodetic application of statistical models extremely useful.
Author : James H. Stapleton
Publisher : John Wiley & Sons
ISBN 13 : 0470317760
Total Pages : 474 pages
Book Rating : 4.4/5 (73 download)
Download or read book Linear Statistical Models written by James H. Stapleton and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear Statistical Models Developed and refined over a period of twenty years, the material in this book offers an especially lucid presentation of linear statistical models. These models lead to what is usually called "multiple regression" or "analysis of variance" methodology, which, in turn, opens up a wide range of applications to the physical, biological, and social sciences, as well as to business, agriculture, and engineering. Unlike similar books on this topic, Linear Statistical Models emphasizes the geometry of vector spaces because of the intuitive insights this approach brings to an understanding of the theory. While the focus is on theory, examples of applications, using the SAS and S-Plus packages, are included. Prerequisites include some familiarity with linear algebra, and probability and statistics at the postcalculus level. Major topics covered include: * Methods of study of random vectors, including the multivariate normal, chi-square, t and F distributions, central and noncentral * The linear model and the basic theory of regression analysis and the analysis of variance * Multiple regression methods, including transformations, analysis of residuals, and asymptotic theory for regression analysis. Separate sections are devoted to robust methods and to the bootstrap. * Simultaneous confidence intervals: Bonferroni, Scheffe, Tukey, and Bechhofer * Analysis of variance, with two- and three-way analysis of variance * Random component models, nested designs, and balanced incomplete block designs * Analysis of frequency data through log-linear models, with emphasis on vector space viewpoint. This chapter alone is sufficient for a course on the analysis of frequency data.
Author : Mykhaylo Andriychuk
Publisher : BoD – Books on Demand
ISBN 13 : 1803558229
Total Pages : 274 pages
Book Rating : 4.8/5 (35 download)
Download or read book Matrix Theory written by Mykhaylo Andriychuk and published by BoD – Books on Demand. This book was released on 2023 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matrix Theory - Classics and Advances examines matrix theory and its application in solving a series of problems related to natural phenomena and applied science. It consists of eleven chapters divided into two sections. Section 1, "Theory and Progress", discusses the classical problems of matrix theory and its contribution to different fields of pure mathematics. Section 2, "Applications", contains the research related to the application of matrix theory in applied science.
Author : Jason Lachniet
Publisher : Lulu.com
ISBN 13 : 0359329640
Total Pages : 156 pages
Book Rating : 4.3/5 (593 download)
Download or read book Introduction to GNU Octave written by Jason Lachniet and published by Lulu.com. This book was released on 2018-11-21 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: A brief introduction to scientific computing with GNU Octave. Designed as a textbook supplement for freshman and sophomore level linear algebra and calculus students.
Author : William K. Pratt
Publisher : CRC Press
ISBN 13 : 1482216701
Total Pages : 750 pages
Book Rating : 4.4/5 (822 download)
Download or read book Introduction to Digital Image Processing written by William K. Pratt and published by CRC Press. This book was released on 2013-09-13 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of digital image processing has migrated from a graduate to a junior or senior level course as students become more proficient in mathematical background earlier in their college education. With that in mind, Introduction to Digital Image Processing is simpler in terms of mathematical derivations and eliminates derivations of advanced s
