Автор: Ashok K Goel; Mateja Jamnik; N Hari Narayanan Название: Diagrammatic Representation and Inference ISBN: 364214599X ISBN-13(EAN): 9783642145995 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 6th International Conference on Theory and Application of Diagrams, Diagrams 2010, held in Portland, OR, USA, in August 2010.
Автор: Nakatsu Название: Diagrammatic Reasoning in AI ISBN: 0470331879 ISBN-13(EAN): 9780470331873 Издательство: Wiley Рейтинг: Цена: 19317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A diagram can be used to graphically demonstrate cause and effect in intelligent systems, in particular, expert systems. Diagrammatic Reasoning in AI explores the use of diagrams, or graphical representations, that show how something works or makes something easier to understand.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Автор: Wojciech Wieczorek Название: Grammatical Inference ISBN: 3319468006 ISBN-13(EAN): 9783319468006 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed.
Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others.
Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>
Автор: Alexander Clark; Fran?ois Coste; Laurent Miclet Название: Grammatical Inference: Algorithms and Applications ISBN: 3540880089 ISBN-13(EAN): 9783540880080 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 9th International Colloquium on Grammatical Inference, ICGI 2008, held in Saint-Malo, France, in September 2008. This title also includes papers on topics ranging from theoretical results of learning algorithms to innovative applications of grammatical inference.
Описание: The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations.
Автор: Josephson Название: Abductive Inference ISBN: 0521575451 ISBN-13(EAN): 9780521575454 Издательство: Cambridge Academ Рейтинг: Цена: 7445.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume makes significant progress in analysing abduction as an information-processing phenomenon, and in describing how AI systems can be built for abductive tasks such as diagnosis.
Автор: Josephson Название: Abductive Inference ISBN: 0521434610 ISBN-13(EAN): 9780521434614 Издательство: Cambridge Academ Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume makes significant progress in analysing abduction as an information-processing phenomenon, and in describing how AI systems can be built for abductive tasks such as diagnosis.
Автор: Catarina Silva; Bernadete Ribeiro Название: Inductive Inference for Large Scale Text Classification ISBN: 3642045324 ISBN-13(EAN): 9783642045325 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.
Автор: Catarina Silva; Bernadete Ribeiro Название: Inductive Inference for Large Scale Text Classification ISBN: 3642261345 ISBN-13(EAN): 9783642261343 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.
Описание: Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton s insight, we may never have made the connection between inference and brief encoding, which is the heart of this work."
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