Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T 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: 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: 158400.00 T 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: Michael Felsberg Название: Probabilistic and Biologically Inspired Feature Representations ISBN: 1681730235 ISBN-13(EAN): 9781681730233 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 46200.00 T Наличие на складе: Невозможна поставка. Описание: Under the title Probabilistic and Biologically Inspired Feature Representations, this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife—they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.
Автор: Danel Jaso Название: Pattern Recognition Techniques, Technology & Applications ISBN: 1681174642 ISBN-13(EAN): 9781681174648 Издательство: Gazelle Book Services Рейтинг: Цена: 217350.00 T Наличие на складе: Невозможна поставка. Описание: This book highlights recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition. The book provides a comprehensive overview of the developments of techniques and approaches on pattern recognition. Pattern recognition is the science of making inferences from perceptual data, using tools from statistics, probability, computational geometry, machine learning, signal processing, and algorithm design. A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition task. Pattern recognition is the imposition of identity on input data, such as speech, images, or a stream of text, by the recognition and delineation of patterns it contains and their relationships. Stages in pattern recognition may involve measurement of the object to identify distinguishing attributes, extraction of features for the defining attributes, and comparison with known patterns to determine a match or mismatch. Pattern recognition has extensive application in astronomy, medicine, robotics, and remote sensing by satellites.
Автор: Vijay Kumar Mago, Nitin Bhatia Название: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies ISBN: 1613504292 ISBN-13(EAN): 9781613504291 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 189420.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Walter J. Scheirer Название: Extreme Value Theory-Based Methods for Visual Recognition ISBN: 1627057005 ISBN-13(EAN): 9781627057004 Издательство: Turpin Рейтинг: Цена: 68930.00 T Наличие на складе: Невозможна поставка. 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.
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: Michael Felsberg Название: Probabilistic and Biologically Inspired Feature Representations ISBN: 1681733668 ISBN-13(EAN): 9781681733661 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66530.00 T Наличие на складе: Невозможна поставка. Описание: pacote do Courseware consiste em duas publicacoes, VeriSMTM - Foundation Courseware e VeriSM - Foundation Study Guide. Este material de treinamento abrange o plano de estudos para a qualificacao da Fundacao VeriSM . O treinamento pode ser entregue em dois dias. Este material didatico e credenciado para preparar o aluno para a certificacao da VeriSM Foundation. O VeriSM Foundation consiste em duas partes: VeriSM Essentials e VeriSM Plus, cada uma cobrindo um dia de treinamento.Os alunos que ja possuem um certificado de Gerenciamento de Servicos (TI) podem se beneficiar do conhecimento que ja possuem. Eles sao o publico-alvo de apenas um treinamento do VeriSM Plus. Ao serem aprovados no exame VeriSM Plus, recebem o certificado VeriSM Foundation.Provedores de treinamento que desejam oferecer um treinamento de um dia sobre principios de gerenciamento de servicos podem decidir oferecer apenas o treinamento VeriSM Essentials. Os alunos que forem aprovados no exame VeriSM Essentials receberao o certificado VeriSM Essentials. Se eles passarem no exame VeriSM Plus mais tarde, receberao automaticamente o certificado VeriSM Foundation.O "courseware" abrange os seguintes topicos:A organizacao do servico (Essentials)Cultura de servico (Essentials)Pessoas e estrutura organizacional (Essentials)O modelo VeriSM (ambos)Praticas Progressivas (Plus)Tecnologias Inovadoras (Plus)O VeriSM e uma abordagem holistica e orientada aos negocios para o Gerenciamento de Servicos, que ajuda a entender o panorama crescente das melhores praticas e como integra-las para oferecer valor ao consumidor.E uma evolucao no pensamento em Gerenciamento de Servicos e oferece uma abordagem atualizada, incluindo as mais recentes praticas e desenvolvimentos tecnologicos, para ajudar as organizacoes a transformar seus negocios para a nova realidade da era digital.O VeriSM e um gerenciamento orientado a valor, evolutivo, responsivo e integrado.VeriSM e uma marca registrada e propriedade da IFDC, a Fundacao Internacional de Competencias Digitais.
Автор: Constantin Papaodysseus Название: Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology ISBN: 1609607864 ISBN-13(EAN): 9781609607869 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 189420.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Jurgen Beyerer, Matthias Richter, Matthias Nagel Название: Pattern Recognition: Introduction, Features, Classifiers and Principles ISBN: 3110537931 ISBN-13(EAN): 9783110537932 Издательство: Walter de Gruyter Цена: 86720.00 T Наличие на складе: Невозможна поставка. Описание: The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features, their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book is to clarifies the concepts so that the reader can easily understand the underlying ideas and the rationale behind the methods. For this purpose, the mathematical treatment of Pattern Recognition is pushed so far that the mechanisms of action become clear and visible, but not farther. Therefore, not all derivations are driven into the last mathematical detail, as a mathematician would expect it. Ideas of proofs are presented instead of complete proofs. From the authors’ point of view, this concept allows to teach the essential ideas of Pattern Recognition with sufficient depth within a relatively lean book. Mathematical methods explained thoroughly Extremely practical approach with many examples Based on over ten years lecture at Karlsruhe Institute of Technology For students but also for practitioners
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: 61480.00 T 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.
Автор: Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun Название: A Guide to Convolutional Neural Networks for Computer Vision ISBN: 1681732785 ISBN-13(EAN): 9781681732787 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 102570.00 T Наличие на складе: Невозможна поставка. Описание: Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision.This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation.This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
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