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.
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: Liu Derong, Apippi Cesare, Zhao Dongbin Название: Frontiers of Intelligent Control and Information Processing ISBN: 9814616877 ISBN-13(EAN): 9789814616874 Издательство: World Scientific Publishing Цена: 23760.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The current research and development in intelligent control and information processing have been driven increasingly by advancements made from fields outside the traditional control areas, into new frontiers of intelligent control and information processing so as to deal with ever more complex systems with ever growing size of data and complexity.
Автор: Yanio Hern?ndez Heredia; Vladimir Mili?n N??ez; Jo Название: Progress in Artificial Intelligence and Pattern Recognition ISBN: 3030011313 ISBN-13(EAN): 9783030011314 Издательство: Springer Рейтинг: Цена: 9222.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018, held in Havana, Cuba, in September 2018. The 42 full papers presented were carefully reviewed and selected from 101 submissions. The papers promote and disseminate ongoing research on mathematical methods and computing techniques for artificial intelligence and pattern recognition, in particular in bioinformatics, cognitive and humanoid vision, computer vision, image analysis and intelligent data analysis, as well as their application in a number of diverse areas such as industry, health, robotics, data mining, opinion mining and sentiment analysis, telecommunications, document analysis, and natural language processing and recognition.
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.
Описание: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies provides a common platform for researchers to present theoretical and applied research findings for enhancing and developing intelligent systems. Through its discussions of advances in and applications of pattern recognition technologies and artificial intelligence, this reference highlights core concepts in biometric imagery, feature recognition, and other related fields, along with their applicability.
Описание: Computer science-especially pattern recognition, signal processing and mathematical algorithms-can offer important information about archaeological finds, information that is otherwise undetectable by the human senses and traditional archaeological approaches. Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology offers state of the art research in computational pattern recognition and digital archaeometry. Computer science researchers in pattern recognition and machine intelligence will find innovative research methodologies combined to create novel and efficient computational systems, offering robust, exact, and reliable performance and results. Archaeologists, conservators, and historians will discover reliable automated methods for quickly reconstructing archaeological materials and benefit from the application of non-destructive, automated processing of archaeological finds.
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