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Author :
Publisher :
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4.:/5 (321 download)
Download or read book A Comparison of New and Old Algorithms for a Mixture Estimation Problem written by and published by . This book was released on 1995 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Quiang Yang
Publisher : Springer
ISBN 13 : 3540366687
Total Pages : 1291 pages
Book Rating : 4.5/5 (43 download)
Download or read book PRICAI 2006: Trends in Artificial Intelligence written by Quiang Yang and published by Springer. This book was released on 2008-02-20 with total page 1291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, held in Guilin, China in August 2006. The book presents 81 revised full papers and 87 revised short papers together with 3 keynote talks. The papers are organized in topical sections on intelligent agents, automated reasoning, machine learning and data mining, natural language processing and speech recognition, computer vision, perception and animation, and more.
Author : Argimiro Arratia
Publisher : Springer Science & Business Media
ISBN 13 : 9462390703
Total Pages : 305 pages
Book Rating : 4.4/5 (623 download)
Download or read book Computational Finance written by Argimiro Arratia and published by Springer Science & Business Media. This book was released on 2014-05-08 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described.
Author : Yoav Freund
Publisher : Springer Science & Business Media
ISBN 13 : 3540879862
Total Pages : 480 pages
Book Rating : 4.5/5 (48 download)
Download or read book Algorithmic Learning Theory written by Yoav Freund and published by Springer Science & Business Media. This book was released on 2008-09-29 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.
Author : SIGART.
Publisher :
ISBN 13 : 9780897918916
Total Pages : 338 pages
Book Rating : 4.9/5 (189 download)
Download or read book Proceedings of the Tenth Annual Conference on Computational Learning Theory written by SIGART. and published by . This book was released on 1997 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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ISBN 13 :
Total Pages : 364 pages
Book Rating : 4.3/5 (91 download)
Download or read book Proceedings of the ... Annual ACM Conference on Computational Learning Theory written by and published by . This book was released on 1997 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ivan Nagy
Publisher : Springer
ISBN 13 : 3319646710
Total Pages : 118 pages
Book Rating : 4.3/5 (196 download)
Download or read book Algorithms and Programs of Dynamic Mixture Estimation written by Ivan Nagy and published by Springer. This book was released on 2017-08-14 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.
Author : Bin Li
Publisher : CRC Press
ISBN 13 : 1482249642
Total Pages : 227 pages
Book Rating : 4.4/5 (822 download)
Download or read book Online Portfolio Selection written by Bin Li and published by CRC Press. This book was released on 2018-10-30 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
Author : Nicolo Cesa-Bianchi
Publisher : Cambridge University Press
ISBN 13 : 113945482X
Total Pages : 4 pages
Book Rating : 4.1/5 (394 download)
Download or read book Prediction, Learning, and Games written by Nicolo Cesa-Bianchi and published by Cambridge University Press. This book was released on 2006-03-13 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
Author : Geoffrey McLachlan
Publisher : John Wiley & Sons
ISBN 13 : 047165406X
Total Pages : 419 pages
Book Rating : 4.4/5 (716 download)
Download or read book Finite Mixture Models written by Geoffrey McLachlan and published by John Wiley & Sons. This book was released on 2004-03-22 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Author : Vy-Thuy-Lynh Hoang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (12 download)
Download or read book Models and Estimation Algorithms for Nonparametric Finite Mixtures with Conditionally Independent Multivariate Component Densities written by Vy-Thuy-Lynh Hoang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently several authors have proposed models and estimation algorithms for finite nonparametric multivariate mixtures, whose identifiability is typically not obvious. Among the considered models, the assumption of independent coordinates conditional on the subpopulation from which each observation is drawn is subject of an increasing attention, in view of the theoretical and practical developments it allows, particularly with multiplicity of variables coming into play in the modern statistical framework. In this work we first consider a more general model assuming independence, conditional on the component, of multivariate blocks of coordinates instead of univariate coordinates, allowing for any dependence structure within these blocks. Consequently, the density functions of these blocks are completely multivariate and nonparametric. We present identifiability arguments and introduce for estimation in this model two methodological algorithms whose computational procedures resemble a true EM algorithm but include an additional density estimation step: a fast algorithm showing empirical efficiency without theoretical justification, and a smoothed algorithm possessing a monotony property as any EM algorithm does, but more computationally demanding. We also discuss computationally efficient methods for estimation and derive some strategies. Next, we consider a multivariate extension of the mixture models used in the framework of multiple hypothesis testings, allowing for a new multivariate version of the False Discovery Rate control. We propose a constrained version of our previous algorithm, specifically designed for this model. The behavior of the EM-type algorithms we propose is studied numerically through several Monte Carlo experiments and high dimensional real data, and compared with existing methods in the literature. Finally, the codes of our new algorithms are progressively implemented as new functions in the publicly-available package mixtools for the R statistical software.
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ISBN 13 :
Total Pages : 780 pages
Book Rating : 4.:/5 (321 download)
Download or read book Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing written by and published by . This book was released on 1995 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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ISBN 13 :
Total Pages : 368 pages
Book Rating : 4.3/5 (91 download)
Download or read book Proceedings written by and published by . This book was released on 1996 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Mark Herbster
Publisher :
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.:/5 (321 download)
Download or read book Learning Algorithms for Tracking Changing Concepts and an Investigation Into the Error Surfaces of Single Artificial Neurons written by Mark Herbster and published by . This book was released on 1998 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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ISBN 13 :
Total Pages : 2948 pages
Book Rating : 4.:/5 (4 download)
Download or read book Applied Science & Technology Index written by and published by . This book was released on 1997 with total page 2948 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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ISBN 13 :
Total Pages : 76 pages
Book Rating : 4.4/5 (721 download)
Download or read book Acta Polytechnica Scandinavica written by and published by . This book was released on 1998 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Bambang Heru Iswanto
Publisher : Logos Verlag Berlin
ISBN 13 : 9783832508098
Total Pages : 0 pages
Book Rating : 4.5/5 (8 download)
Download or read book New Algorithms for Learning of Mixture Models and Their Application for Classification and Density Estimation written by Bambang Heru Iswanto and published by Logos Verlag Berlin. This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixture model is known as a convenient way for modelling the probability density function in statistics. Recently, the method is adopted by machine learning communities in a variety of application settings such as cluster analysis, classification, density estimation and function approximation. This book concerns with learning algorithms of the mixture models for density estimation and classification tasks. Special attention is given for the semi-supervised learning and active learning methods which are very important in many practical settings. The presented learning methods attempt to reduce the size of labelled data sets required to achieve certain level of classification performance.
