The Random Projection Method - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Random Projection Method write by Santosh S. Vempala. This book was released on 2005-02-24. The Random Projection Method available in PDF, EPUB and Kindle. 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
The Practice of Entrepreneurship - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Practice of Entrepreneurship write by Geoffrey Grant Meredith. This book was released on 1982. The Practice of Entrepreneurship available in PDF, EPUB and Kindle. 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
The Random Projection Method - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Random Projection Method write by Santosh Srinivas Vempala. This book was released on 2004. The Random Projection Method available in PDF, EPUB and Kindle. 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 Essentials of Machine Learning in Finance and Accounting
The Essentials of Machine Learning in Finance and Accounting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Essentials of Machine Learning in Finance and Accounting write by Mohammad Zoynul Abedin. This book was released on 2021-06-20. The Essentials of Machine Learning in Finance and Accounting available in PDF, EPUB and Kindle. 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.
Foundations of Data Science
Foundations of Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Data Science write by Avrim Blum. This book was released on 2020-01-23. Foundations of Data Science available in PDF, EPUB and Kindle. 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.
