The Random Projection Method
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The Random Projection Method
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Author : Santosh S. Vempala
language : en
Publisher: American Mathematical Soc.
Release Date : 2005-02-24
The Random Projection Method written by Santosh S. Vempala and has been published by American Mathematical Soc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-24 with Mathematics categories.
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 Random Projection Method
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Author : Santosh Srinivas Vempala
language : en
Publisher:
Release Date : 2004
The Random Projection Method written by Santosh Srinivas Vempala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with MATHEMATICS categories.
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.
The Practice Of Entrepreneurship
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Author : Geoffrey Grant Meredith
language : en
Publisher:
Release Date : 1982
The Practice Of Entrepreneurship written by Geoffrey Grant Meredith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with Business & Economics categories.
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.
Experimental Study Of Random Projections Below The Jl Limit
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Author : Xiuyi Ye (Software engineer)
language : en
Publisher:
Release Date : 2015
Experimental Study Of Random Projections Below The Jl Limit written by Xiuyi Ye (Software engineer) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Random projection is a method used to reduce dimensionality of desired objects with pair-wise distances preserved at a relatively high probability. The mathematical theory behind this is called the Johnson-Lindenstrauss (JL) lemma. So, the basic idea of the JL lemma is that a set of points in a high dimensional space p are randomly projected down to a lower dimensional space q. This q can be as low as q0 to still make sure that with a certain probability the projected pair-wise distances are within [plus-minus][epsilon], of the pairwise distances before the projection, where plus or minus [eplison] is usually a very small value. This technique has already been used in a variety of areas like clustering, image and text data processing. Lots of researchers have already studied the properties and performance of the JL lemma above q0 (q is usually called the JL limit or JL bound), where q = p-1, p-2,..., q0, but no research has investigated using the JL lemma below the JL limit (q = q0-1, q0-2,..., 2). With much lower dimension, the data processing, storing almost everything is going to be so much easier. We can visualize the clustering information about data sets in 2D plots. One thing should not be forgotten is that the distance preservation is probabilistic. How well will the distances being preserved below the JL bound? Will it affect or even completely destroy the cluster structure after the projection? What is a good projection method? We are going to study and answer these questions as much as we can in this thesis.
The Essentials Of Machine Learning In Finance And Accounting
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Routledge
Release Date : 2021-06-20
The Essentials Of Machine Learning In Finance And Accounting written by Mohammad Zoynul Abedin and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-20 with Business & Economics categories.
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.
Subspace Latent Structure And Feature Selection
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Author : Craig Saunders
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-16
Subspace Latent Structure And Feature Selection written by Craig Saunders and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-16 with Computers categories.
Many of the papers in this proceedings volume were presented at the PASCAL Workshop entitled Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimization Perspectives which took place in Bohinj, Slovenia during February, 23–25 2005.
Artificial Intelligence And Soft Computing
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Author : Leszek Rutkowski
language : en
Publisher: Springer
Release Date : 2018-05-24
Artificial Intelligence And Soft Computing written by Leszek Rutkowski and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-24 with Computers categories.
The two-volume set LNAI 10841 and LNAI 10842 constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, held in Zakopane, Poland in June 2018. The 140 revised full papers presented were carefully reviewed and selected from 242 submissions. The papers included in the first volume are organized in the following three parts: neural networks and their applications; evolutionary algorithms and their applications; and pattern classification.
Trends And Applications In Knowledge Discovery And Data Mining
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Author : Wei Lu
language : en
Publisher: Springer Nature
Release Date : 2020-10-14
Trends And Applications In Knowledge Discovery And Data Mining written by Wei Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-14 with Computers categories.
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)
Big Data And Security
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Author : Yuan Tian
language : en
Publisher: Springer Nature
Release Date : 2021-06-21
Big Data And Security written by Yuan Tian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-21 with Computers categories.
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.
Rank Deficient And Discrete Ill Posed Problems
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Author : Per Christian Hansen
language : en
Publisher: SIAM
Release Date : 2005-01-01
Rank Deficient And Discrete Ill Posed Problems written by Per Christian Hansen and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-01 with Mathematics categories.
Here is an overview of modern computational stabilization methods for linear inversion, with applications to a variety of problems in audio processing, medical imaging, tomography, seismology, astronomy, and other areas. Rank-deficient problems involve matrices that are either exactly or nearly rank deficient. Such problems often arise in connection with noise suppression and other problems where the goal is to suppress unwanted disturbances of the given measurements. Discrete ill-posed problems arise in connection with the numerical treatment of inverse problems, where one typically wants to compute information about some interior properties using exterior measurements. Examples of inverse problems are image restoration and tomography, where one needs to improve blurred images or reconstruct pictures from raw data. This book describes, in a common framework, new and existing numerical methods for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties of the algorithms and on the efficiency and reliability of the computations. The setting is that of numerical linear algebra rather than abstract functional analysis, and the theoretical development is complemented with numerical examples and figures that illustrate the features of the various algorithms.