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| Author | : Peter D. Grünwald |
| Publisher | : MIT Press |
| Total Pages | : 464 |
| Release | : 2005 |
| Genre | : Computers |
| ISBN | : 9780262072625 |
A source book for state-of-the-art MDL, including an extensive tutorial and recent theoretical advances and practical applications in fields ranging from bioinformatics to psychology.
| Author | : Peter D. Grünwald |
| Publisher | : MIT Press |
| Total Pages | : 736 |
| Release | : 2007 |
| Genre | : Minimum description length (Information theory). |
| ISBN | : 0262072815 |
This introduction to the MDL Principle provides a reference accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection.
| Author | : Ming Li |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 655 |
| Release | : 2013-03-09 |
| Genre | : Mathematics |
| ISBN | : 1475726066 |
Briefly, we review the basic elements of computability theory and prob ability theory that are required. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity. This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description (or the number of bits of information in it) is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity. This body of knowledge may be called algo rithmic complexity theory. The theory of Martin-Lof tests for random ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion theoretic properties of Kolmogorov complexity (relations with Godel's incompleteness result), and the Kolmogorov complexity version of infor mation theory, which we may call "algorithmic information theory" or "absolute information theory. " The treatment of algorithmic probability theory in Chapter 4 presup poses Sections 1. 6, 1. 11. 2, and Chapter 3 (at least Sections 3. 1 through 3. 4).
| Author | : Michael R. Berthold |
| Publisher | : Springer |
| Total Pages | : 588 |
| Release | : 2020-04-02 |
| Genre | : Computers |
| ISBN | : 9783030445836 |
This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
| Author | : Shai Shalev-Shwartz |
| Publisher | : Cambridge University Press |
| Total Pages | : 415 |
| Release | : 2014-05-19 |
| Genre | : Computers |
| ISBN | : 1107057132 |
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
| Author | : American Bar Association. House of Delegates |
| Publisher | : American Bar Association |
| Total Pages | : 216 |
| Release | : 2007 |
| Genre | : Law |
| ISBN | : 9781590318737 |
The Model Rules of Professional Conduct provides an up-to-date resource for information on legal ethics. Federal, state and local courts in all jurisdictions look to the Rules for guidance in solving lawyer malpractice cases, disciplinary actions, disqualification issues, sanctions questions and much more. In this volume, black-letter Rules of Professional Conduct are followed by numbered Comments that explain each Rule's purpose and provide suggestions for its practical application. The Rules will help you identify proper conduct in a variety of given situations, review those instances where discretionary action is possible, and define the nature of the relationship between you and your clients, colleagues and the courts.
| Author | : Lynn Harold Loomis |
| Publisher | : World Scientific Publishing Company |
| Total Pages | : 595 |
| Release | : 2014-02-26 |
| Genre | : Mathematics |
| ISBN | : 9814583952 |
An authorised reissue of the long out of print classic textbook, Advanced Calculus by the late Dr Lynn Loomis and Dr Shlomo Sternberg both of Harvard University has been a revered but hard to find textbook for the advanced calculus course for decades.This book is based on an honors course in advanced calculus that the authors gave in the 1960's. The foundational material, presented in the unstarred sections of Chapters 1 through 11, was normally covered, but different applications of this basic material were stressed from year to year, and the book therefore contains more material than was covered in any one year. It can accordingly be used (with omissions) as a text for a year's course in advanced calculus, or as a text for a three-semester introduction to analysis.The prerequisites are a good grounding in the calculus of one variable from a mathematically rigorous point of view, together with some acquaintance with linear algebra. The reader should be familiar with limit and continuity type arguments and have a certain amount of mathematical sophistication. As possible introductory texts, we mention Differential and Integral Calculus by R Courant, Calculus by T Apostol, Calculus by M Spivak, and Pure Mathematics by G Hardy. The reader should also have some experience with partial derivatives.In overall plan the book divides roughly into a first half which develops the calculus (principally the differential calculus) in the setting of normed vector spaces, and a second half which deals with the calculus of differentiable manifolds.
| Author | : Licheng Jiao |
| Publisher | : Springer |
| Total Pages | : 1030 |
| Release | : 2006-09-28 |
| Genre | : Computers |
| ISBN | : 3540459022 |
This is volume I of the proceedings of the Second International Conference on Natural Computation, ICNC 2006. After a demanding review process 168 carefully revised full papers and 86 revised short papers were selected from 1915 submissions for presentation in two volumes. This first volume includes 130 papers related to artificial neural networks, natural neural systems and cognitive science, neural network applications, as well as evolutionary computation: theory and algorithms.
| Author | : Jorma Rissanen |
| Publisher | : World Scientific |
| Total Pages | : 191 |
| Release | : 1998-10-07 |
| Genre | : Technology & Engineering |
| ISBN | : 9814507407 |
This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.
| Author | : Christoph Molnar |
| Publisher | : Lulu.com |
| Total Pages | : 320 |
| Release | : 2020 |
| Genre | : Artificial intelligence |
| ISBN | : 0244768528 |
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
