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: Bishop, Christopher M. Название: Neural Networks for Pattern Recognition ISBN: 0198538642 ISBN-13(EAN): 9780198538646 Издательство: Oxford Academ Рейтинг: Цена: 13939.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.
Автор: Lionel Prevost; Simone Marinai; Friedhelm Schwenke Название: Artificial Neural Networks in Pattern Recognition ISBN: 3540699384 ISBN-13(EAN): 9783540699385 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the Third TC3 IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008, held in Paris, France, in July 2008. This book features 18 revised full papers and 11 revised poster papers which are organized in topical sections on unsupervised learning, supervised learning, and applications.
Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intelligence and decision making. A correlation machine is defined and built using multi-layer perceptron network, principal component analysis, Gaussian Mixture models, genetic algorithms, expectation maximization technique, simulated annealing and particle swarm optimization. Furthermore, a causal machine is defined and built using multi-layer perceptron, radial basis function, Bayesian statistics and Hybrid Monte Carlo methods. Both these machines are used to build a Granger non-linear causality model. In addition, the Neyman-Rubin, Pearl and Granger causal models are studied and are unified. The automatic relevance determination is also applied to extend Granger causality framework to the non-linear domain. The concept of rational decision making is studied, and the theory of flexibly-bounded rationality is used to extend the theory of bounded rationality within the principle of the indivisibility of rationality. The theory of the marginalization of irrationality for decision making is also introduced to deal with satisficing within irrational conditions. The methods proposed are applied in biomedical engineering, condition monitoring and for modelling interstate conflict.
Автор: Brian D. Ripley Название: Pattern Recognition and Neural Networks ISBN: 0521717701 ISBN-13(EAN): 9780521717700 Издательство: Cambridge Academ Рейтинг: Цена: 7762.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/
Автор: Pal Sankar Kumar & Pal Amita Название: Pattern Recognition And Big Data ISBN: 9813144548 ISBN-13(EAN): 9789813144545 Издательство: World Scientific Publishing Рейтинг: Цена: 25978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications.Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.
Описание: A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the ""average."" From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.
Автор: Sankar K. Pal; Shubhra S. Ray; Avatharam Ganivada Название: Granular Neural Networks, Pattern Recognition and Bioinformatics ISBN: 3319571133 ISBN-13(EAN): 9783319571133 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models.
Автор: Schwenker Название: Artificial Neural Networks in Pattern Recognition ISBN: 3319461818 ISBN-13(EAN): 9783319461816 Издательство: Springer Рейтинг: Цена: 8106.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016.
Автор: Neamat El Gayar; Friedhelm Schwenker; Cheng Suen Название: Artificial Neural Networks in Pattern Recognition ISBN: 331911655X ISBN-13(EAN): 9783319116556 Издательство: Springer Рейтинг: Цена: 7826.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014.
Описание: This book covers hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Includes basic theory, use of type-2 fuzzy models, optimization of type-2 fuzzy systems and modular neural networks and more.
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