The Random Projection Method

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Publisher : American Mathematical Soc.
ISBN 13 : 0821837931
Total Pages : 120 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis The Random Projection Method by : Santosh S. Vempala

Download or read book The Random Projection Method written by Santosh S. Vempala and published by American Mathematical Soc.. This book was released on 2005-02-24 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random projection is a simple geometric technique for reducing the dimensionality of a set of points in Euclidean space while preserving pairwise distances approximately. The technique plays a key role in several breakthrough developments in the field of algorithms. In other cases, it provides elegant alternative proofs. The book begins with an elementary description of the technique and its basic properties. Then it develops the method in the context of applications, which are divided into three groups. The first group consists of combinatorial optimization problems such as maxcut, graph coloring, minimum multicut, graph bandwidth and VLSI layout. Presented in this context is the theory of Euclidean embeddings of graphs. The next group is machine learning problems, specifically, learning intersections of halfspaces and learning large margin hypotheses. The projection method is further refined for the latter application. The last set consists of problems inspired by information retrieval, namely, nearest neighbor search, geometric clustering and efficient low-rank approximation. Motivated by the first two applications, an extension of random projection to the hypercube is developed here. Throughout the book, random projection is used as a way to understand, simplify and connect progress on these important and seemingly unrelated problems. The book is suitable for graduate students and research mathematicians interested in computational geometry.

The Practice of Entrepreneurship

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Publisher :
ISBN 13 : 9789221028390
Total Pages : 214 pages
Book Rating : 4.0/5 (283 download)

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Book Synopsis The Practice of Entrepreneurship by : Geoffrey Grant Meredith

Download or read book The Practice of Entrepreneurship written by Geoffrey Grant Meredith and published by . This book was released on 1982 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended to help individuals in self development for business ownership, this volume presents personal characteristics, planning and control and the variety and use of resources for the entrepreneur. Includes numerous checklists, formula and graphic analytical devices and practical techniques.

The Random Projection Method

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Publisher :
ISBN 13 : 9781470417772
Total Pages : 105 pages
Book Rating : 4.4/5 (177 download)

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Book Synopsis The Random Projection Method by : Santosh Srinivas Vempala

Download or read book The Random Projection Method written by Santosh Srinivas Vempala and published by . This book was released on 2004 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random projection is a simple geometric technique for reducing the dimensionality of a set of points in Euclidean space while preserving pairwise distances approximately. The technique plays a key role in several breakthrough developments in the field of algorithms. In other cases, it provides elegant alternative proofs. The book begins with an elementary description of the technique and its basic properties. Then it develops the method in the context of applications, which are divided into three groups. The first group consists of combinatorial optimization problems such as maxcut, graph colo.

Foundations of Data Science

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Publisher : Cambridge University Press
ISBN 13 : 1108617360
Total Pages : 433 pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis Foundations of Data Science by : Avrim Blum

Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

The Essentials of Machine Learning in Finance and Accounting

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Publisher : Routledge
ISBN 13 : 1000394123
Total Pages : 275 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis The Essentials of Machine Learning in Finance and Accounting by : Mohammad Zoynul Abedin

Download or read book The Essentials of Machine Learning in Finance and Accounting written by Mohammad Zoynul Abedin and published by Routledge. This book was released on 2021-06-20 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

High-Dimensional Probability

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Publisher : Cambridge University Press
ISBN 13 : 1108415199
Total Pages : 299 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis High-Dimensional Probability by : Roman Vershynin

Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Artificial Intelligence and Soft Computing

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Publisher : Springer Nature
ISBN 13 : 3030614018
Total Pages : 741 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Artificial Intelligence and Soft Computing by : Leszek Rutkowski

Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer Nature. This book was released on 2020-10-20 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 12415 and 12416 constitutes the refereed proceedings of of the 19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020. The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts: ​neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; bioinformatics, biometrics and medical applications; artificial intelligence in modeling and simulation. The papers included in the second volume are organized in the following four parts: computer vision, image and speech analysis; data mining; various problems of artificial intelligence; agent systems, robotics and control. *The conference was held virtually due to the COVID-19 pandemic.

Spectral Algorithms

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Publisher : Now Publishers Inc
ISBN 13 : 1601982747
Total Pages : 153 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Spectral Algorithms by : Ravindran Kannan

Download or read book Spectral Algorithms written by Ravindran Kannan and published by Now Publishers Inc. This book was released on 2009 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral methods refer to the use of eigenvalues, eigenvectors, singular values and singular vectors. They are widely used in Engineering, Applied Mathematics and Statistics. More recently, spectral methods have found numerous applications in Computer Science to "discrete" as well as "continuous" problems. Spectral Algorithms describes modern applications of spectral methods, and novel algorithms for estimating spectral parameters. The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations. A feature of many modern applications is the massive amount of input data. While sophisticated algorithms for matrix computations have been developed over a century, a more recent development is algorithms based on "sampling on the fly" from massive matrices. Good estimates of singular values and low rank approximations of the whole matrix can be provably derived from a sample. The main emphasis in the second part of the book is to present these sampling methods with rigorous error bounds. It also presents recent extensions of spectral methods from matrices to tensors and their applications to some combinatorial optimization problems.

Big Data and Security

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Publisher : Springer Nature
ISBN 13 : 9811631506
Total Pages : 665 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Big Data and Security by : Yuan Tian

Download or read book Big Data and Security written by Yuan Tian and published by Springer Nature. This book was released on 2021-06-21 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Conference on Big Data and Security, ICBDS 2020, held in Singapore, Singapore, in December 2020. The 44 revised full papers and 8 short papers were carefully reviewed and selected out of 153 submissions. The papers included in this book are organized according to the topical sections on cybersecurity and privacy, big data, blockchain and internet of things, and artificial intelligence/ machine learning security.

Proceedings of ELM 2018

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Publisher : Springer
ISBN 13 : 3030233073
Total Pages : 347 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Proceedings of ELM 2018 by : Jiuwen Cao

Download or read book Proceedings of ELM 2018 written by Jiuwen Cao and published by Springer. This book was released on 2019-06-29 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21–23, 2018. This conference provided a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental “learning particles” filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM.

Similarity Search and Applications

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Publisher : Springer Nature
ISBN 13 : 3030896579
Total Pages : 415 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Similarity Search and Applications by : Nora Reyes

Download or read book Similarity Search and Applications written by Nora Reyes and published by Springer Nature. This book was released on 2021-10-21 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th International Conference on Similarity Search and Applications, SISAP 2021, held in Dortmund, Germany, in September/October 2021. The conference was held virtually due to the COVID-19 pandemic.The 23 full papers presented together with 5 short and 3 doctoral symposium papers were carefully reviewed and selected from 50 submissions. The papers are organized in the topical sections named: ​Similarity Search and Retrieval; Intrinsic Dimensionality; Clustering and Classification; Applications of Similarity Search; Similarity Search in Graph-Structured Data; Doctoral Symposium.

The Probabilistic Method

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Publisher : John Wiley & Sons
ISBN 13 : 1119062071
Total Pages : 400 pages
Book Rating : 4.1/5 (19 download)

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Book Synopsis The Probabilistic Method by : Noga Alon

Download or read book The Probabilistic Method written by Noga Alon and published by John Wiley & Sons. This book was released on 2015-11-02 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Third Edition “Researchers of any kind of extremal combinatorics or theoretical computer science will welcome the new edition of this book.” - MAA Reviews Maintaining a standard of excellence that establishes The Probabilistic Method as the leading reference on probabilistic methods in combinatorics, the Fourth Edition continues to feature a clear writing style, illustrative examples, and illuminating exercises. The new edition includes numerous updates to reflect the most recent developments and advances in discrete mathematics and the connections to other areas in mathematics, theoretical computer science, and statistical physics. Emphasizing the methodology and techniques that enable problem-solving, The Probabilistic Method, Fourth Edition begins with a description of tools applied to probabilistic arguments, including basic techniques that use expectation and variance as well as the more advanced applications of martingales and correlation inequalities. The authors explore where probabilistic techniques have been applied successfully and also examine topical coverage such as discrepancy and random graphs, circuit complexity, computational geometry, and derandomization of randomized algorithms. Written by two well-known authorities in the field, the Fourth Edition features: Additional exercises throughout with hints and solutions to select problems in an appendix to help readers obtain a deeper understanding of the best methods and techniques New coverage on topics such as the Local Lemma, Six Standard Deviations result in Discrepancy Theory, Property B, and graph limits Updated sections to reflect major developments on the newest topics, discussions of the hypergraph container method, and many new references and improved results The Probabilistic Method, Fourth Edition is an ideal textbook for upper-undergraduate and graduate-level students majoring in mathematics, computer science, operations research, and statistics. The Fourth Edition is also an excellent reference for researchers and combinatorists who use probabilistic methods, discrete mathematics, and number theory. Noga Alon, PhD, is Baumritter Professor of Mathematics and Computer Science at Tel Aviv University. He is a member of the Israel National Academy of Sciences and Academia Europaea. A coeditor of the journal Random Structures and Algorithms, Dr. Alon is the recipient of the Polya Prize, The Gödel Prize, The Israel Prize, and the EMET Prize. Joel H. Spencer, PhD, is Professor of Mathematics and Computer Science at the Courant Institute of New York University. He is the cofounder and coeditor of the journal Random Structures and Algorithms and is a Sloane Foundation Fellow. Dr. Spencer has written more than 200 published articles and is the coauthor of Ramsey Theory, Second Edition, also published by Wiley.

A Dynamical Approach to Random Matrix Theory

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Publisher : American Mathematical Soc.
ISBN 13 : 1470436485
Total Pages : 226 pages
Book Rating : 4.4/5 (74 download)

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Book Synopsis A Dynamical Approach to Random Matrix Theory by : László Erdős

Download or read book A Dynamical Approach to Random Matrix Theory written by László Erdős and published by American Mathematical Soc.. This book was released on 2017-08-30 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: A co-publication of the AMS and the Courant Institute of Mathematical Sciences at New York University This book is a concise and self-contained introduction of recent techniques to prove local spectral universality for large random matrices. Random matrix theory is a fast expanding research area, and this book mainly focuses on the methods that the authors participated in developing over the past few years. Many other interesting topics are not included, and neither are several new developments within the framework of these methods. The authors have chosen instead to present key concepts that they believe are the core of these methods and should be relevant for future applications. They keep technicalities to a minimum to make the book accessible to graduate students. With this in mind, they include in this book the basic notions and tools for high-dimensional analysis, such as large deviation, entropy, Dirichlet form, and the logarithmic Sobolev inequality. This manuscript has been developed and continuously improved over the last five years. The authors have taught this material in several regular graduate courses at Harvard, Munich, and Vienna, in addition to various summer schools and short courses. Titles in this series are co-published with the Courant Institute of Mathematical Sciences at New York University.

Pattern Recognition Applications and Methods

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Publisher : Springer
ISBN 13 : 3319936476
Total Pages : 235 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Pattern Recognition Applications and Methods by : Maria De Marsico

Download or read book Pattern Recognition Applications and Methods written by Maria De Marsico and published by Springer. This book was released on 2018-06-15 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains revised and extended versions of selected papers from the 6th International Conference on Pattern Recognition, ICPRAM 2017, held in Porto, Portugal, in February 2017. The 13 full papers presented were carefully reviewed and selected from 139 initial submissions. They aim at making visible and understandable the relevant trends of current research on pattern recognition.

Entropy Randomization in Machine Learning

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Publisher : CRC Press
ISBN 13 : 100062871X
Total Pages : 405 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Entropy Randomization in Machine Learning by : Yuri S. Popkov

Download or read book Entropy Randomization in Machine Learning written by Yuri S. Popkov and published by CRC Press. This book was released on 2022-08-09 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning, Entropy Randomization in Machine Learning considers several applications to binary classification, modelling the dynamics of the Earth’s population, predicting seasonal electric load fluctuations of power supply systems, and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning, researchers and engineers involved in the development of applied machine learning systems, and researchers of forecasting problems in various fields.

Trends and Applications in Knowledge Discovery and Data Mining

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Publisher : Springer Nature
ISBN 13 : 3030604705
Total Pages : 193 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Trends and Applications in Knowledge Discovery and Data Mining by : Wei Lu

Download or read book Trends and Applications in Knowledge Discovery and Data Mining written by Wei Lu and published by Springer Nature. This book was released on 2020-10-14 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, in Singapore, Singapore, in May 2020. The 17 revised full papers presented were carefully reviewed and selected from a total of 50 submissions. The five workshops were as follows: · First International Workshop on Literature-Based Discovery (LBD 2020) · Workshop on Data Science for Fake News (DSFN 2020) · Learning Data Representation for Clustering (LDRC 2020) · Ninth Workshop on Biologically Inspired Techniques for Data Mining (BDM · 2020) · First Pacific Asia Workshop on Game Intelligence & Informatics (GII 2020)

Iterative Methods for Sparse Linear Systems

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Publisher : SIAM
ISBN 13 : 0898715342
Total Pages : 537 pages
Book Rating : 4.8/5 (987 download)

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Book Synopsis Iterative Methods for Sparse Linear Systems by : Yousef Saad

Download or read book Iterative Methods for Sparse Linear Systems written by Yousef Saad and published by SIAM. This book was released on 2003-04-01 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- General.