Download or read book Efficient Online Learning Algorithms for Total Least Square Problems written by Xiangyu Kong and published by Springer Nature. This book was released on with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Total Least Squares Problem written by Sabine Van Huffel and published by SIAM. This book was released on 1991-01-01 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book devoted entirely to total least squares. The authors give a unified presentation of the TLS problem. A description of its basic principles are given, the various algebraic, statistical and sensitivity properties of the problem are discussed, and generalizations are presented. Applications are surveyed to facilitate uses in an even wider range of applications. Whenever possible, comparison is made with the well-known least squares methods. A basic knowledge of numerical linear algebra, matrix computations, and some notion of elementary statistics is required of the reader; however, some background material is included to make the book reasonably self-contained.
Download or read book Towards Better Understanding of Algorithms and Complexity of Some Learning Problems written by Xin Yang and published by . This book was released on 2020 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present several novel results on computational problems related to supervised learning.We focus on the computational resources required by algorithms to solve learning problems. The computational resources we consider are running time, memory usage and query complexity, which is the number of positions in the input that the algorithm needs to check. Some contributions include: time-space tradeoff lower bounds for problems of learning from uniformly random labelled examples. With our methods we can obtain bounds for learning concept classes of finite functions from random evaluations even when the sample space of random inputs can be significantly smaller than the concept class of functions and the function values can be from an arbitrary finite set. A simple and efficient algorithm for approximating the John Ellipsoid of a symmetric polytope. Our algorithm is near optimal in the sense that our time complexity matches the current best verification algorithm. Experimental results suggest that our algorithm significantly outperforms existing algorithms.The first algorithm for the total least squares problem, a variant of the ordinary least squares problem, that runs in time proportional to the sparsity of the input. The core to developing our algorithm involves recent advances in randomized linear algebra. \item A generic space efficient algorithm that is based on deterministic decision trees. The first algorithm for the linear bandits problem with prior constraints.
Download or read book Efficiency and Scalability Methods for Computational Intellect written by Igelnik, Boris and published by IGI Global. This book was released on 2013-04-30 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational modeling and simulation has developed and expanded into a diverse range of fields such as digital signal processing, image processing, robotics, systems biology, and many more; enhancing the need for a diversifying problem solving applications in this area. Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.
Download or read book Fast Algorithms for Structured Least Squares and Total Least Squares Problems written by and published by DIANE Publishing. This book was released on with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Algorithms for Least Squares Type Problems with Bounded Uncertainties written by Stanford University. Computer Science Department. Scientific Computing and Computational Mathematics Program and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Download or read book Algorithms and Analysis for the Efficient Solution of Large Scale Linear Least Squares Problems written by Jocelyn T. Chi and published by . This book was released on 2021 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Collaborative Computing Networking Applications and Worksharing written by Honghao Gao and published by Springer Nature. This book was released on 2022-01-01 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the refereed proceedings of the 17th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 62 full papers and 7 short papers presented were carefully reviewed and selected from 206 submissions. The papers reflect the conference sessions as follows: Optimization for Collaborate System; Optimization based on Collaborative Computing; UVA and Traffic system; Recommendation System; Recommendation System & Network and Security; Network and Security; Network and Security & IoT and Social Networks; IoT and Social Networks & Images handling and human recognition; Images handling and human recognition & Edge Computing; Edge Computing; Edge Computing & Collaborative working; Collaborative working & Deep Learning and application; Deep Learning and application; Deep Learning and application; Deep Learning and application & UVA.
Download or read book Proceedings of ELM 2015 Volume 1 written by Jiuwen Cao and published by Springer. This book was released on 2015-12-31 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
Download or read book Provably Efficient Methods for Large scale Learning written by Shuo Yang (Ph. D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The scale of machine learning problems grows rapidly in recent years and calls for efficient methods. In this dissertation, we propose simple and efficient methods for various large-scale learning problems. We start with a standard supervised learning problem of solving quadratic regression. In Chapter 2, we show that by utilizing the quadratic structure and a novel gradient estimation algorithm, we can solve sparse quadratic regression with sub-quadratic time complexity and near-optimal sample complexity. We then move to online learning problems. In Chapter 3, we identify a weak assumption and theoretically prove that the standard UCB algorithm efficiently learns from inconsistent human preferences with nearly optimal regret; in Chapter 4 we propose an approximate maximum inner product search data structure for adaptive queries and present two efficient algorithms that achieve sublinear time complexity for linear bandits, which is especially desirable for extremely large and slowly changing action sets. In Chapter 5, we study how to efficiently use privileged features with deep learning models. We present an efficient learning algorithm to exploit privileged features that are not available during testing time. We conduct comprehensive empirical evaluations and present rigorous analysis for linear models to build theoretical insights. It provides a general algorithmic paradigm that can be integrated with many other machine learning methods
Download or read book Deep Learning for Hyperspectral Image Analysis and Classification written by Linmi Tao and published by Springer Nature. This book was released on 2021-02-20 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
Download or read book Numerical Algorithms for Estimating Least Squares Problems written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The solution of least squares estimation problems is of great importance in the areas of numerical linear algebra, computational statistics and econometrics. The design and analysis of numerically stable and computationally efficient methods for solving such least squares problems is considered. The main computational tool used for the estimation of the least squares solutions is the QR decomposition, or the generalized QR decomposition. Specifically, emphasis is given to the design of sequential and parallel strategies for computing the main matrix factorizations which arise in the estimation procedures. The strategies are based on block-generalizations of the Givens sequences and efficiently exploit the structure of the matrices. An efficient minimum spanning tree algorithm is proposed for computing the QR decomposition of a set of matrices which have common columns. Heuristic strategies are also considered. Several computationally efficient sequential algorithms for block downdating of the least squares solutions are designed, implemented and analyzed. A parallel algorithm based on the best sequential approach for downdating the QR decomposition is also proposed. Within the context of block up-downdating, efficient serial and parallel algorithms for computing the estimators of the general linear and seemingly unrelated regression models after been updated with new observations are proposed. The algorithms are based on orthogonal factorizations and are rich in BLAS-3 computations. Experimental results which support the theoretical derived complexities of the new algorithms are presented. The comparison of the new algorithms with the corresponding LAPACK routines is also performed. The parallel algorithms utilize efficient load balanced distribution over the processors and are found to be scalable and efficient for large-scale least squares problems. It is expected that the proposed block-algorithms will facilitate the solution of computationally intensive statisti.
Download or read book New Trends in Technologies written by Er Meng Joo and published by BoD – Books on Demand. This book was released on 2010-11-02 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: The grandest accomplishments of engineering took place in the twentieth century. The widespread development and distribution of electricity and clean water, automobiles and airplanes, radio and television, spacecraft and lasers, antibiotics and medical imaging, computers and the Internet are just some of the highlights from a century in which engineering revolutionized and improved virtually every aspect of human life. In this book, the authors provide a glimpse of the new trends of technologies pertaining to control, management, computational intelligence and network systems.
Download or read book Wavelet Numerical Method and Its Applications in Nonlinear Problems written by You-He Zhou and published by Springer Nature. This book was released on 2021-03-09 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the basic theory of wavelets and some related algorithms in an easy-to-understand language from the perspective of an engineer rather than a mathematician. In this book, the wavelet solution schemes are systematically established and introduced for solving general linear and nonlinear initial boundary value problems in engineering, including the technique of boundary extension in approximating interval-bounded functions, the calculation method for various connection coefficients, the single-point Gaussian integration method in calculating the coefficients of wavelet expansions and unique treatments on nonlinear terms in differential equations. At the same time, this book is supplemented by a large number of numerical examples to specifically explain procedures and characteristics of the method, as well as detailed treatments for specific problems. Different from most of the current monographs focusing on the basic theory of wavelets, it focuses on the use of wavelet-based numerical methods developed by the author over the years. Even for the necessary basic theory of wavelet in engineering applications, this book is based on the author’s own understanding in plain language, instead of a relatively difficult professional mathematical description. This book is very suitable for students, researchers and technical personnel who only want to need the minimal knowledge of wavelet method to solve specific problems in engineering.
Download or read book Engineering Applications of Discrete Element Method written by Xuewen Wang and published by Springer Nature. This book was released on 2020-09-10 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the engineering application of the discrete element method (DEM), especially the simulation analysis of the typical equipment (scraper conveyor, coal silos, subsoiler) in the coal and agricultural machinery. In this book, the DEM is applied to build rigid and loose coupling model, and the kinematic effect of the bulk materials, the mechanical effect of the interaction between the bulk materials, and the mechanical equipment in the operation process of the relevant equipment are studied. On this basis, the optimization design strategy of the relevant structure is proposed. This book effectively promotes the application of DEM in engineering, analyzes the operation state, failure mechanism, and operation effect of related equipment in operation, and provides theoretical basis for the optimal design of equipment. The book is intended for undergraduate and graduate students who are interested in mechanical engineering, researchers investigating coal and agricultural machinery, and engineers working on designing related equipments.
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 432 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
