Scalable Pattern Recognition Algorithms, Pradipta Maji; Sushmita Paul
Автор: Seraf?n Moral; Olivier Pivert; Daniel S?nchez; Nic Название: Scalable Uncertainty Management ISBN: 3319675818 ISBN-13(EAN): 9783319675817 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017.
Автор: Sergio Greco Название: Scalable Uncertainty Management ISBN: 3540879927 ISBN-13(EAN): 9783540879923 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the Second International Conference on Scalable Uncertainty Management, SUM 2008, held in Naples, Italy, in October 2008.
Автор: Lluis Godo; Andrea Pugliese Название: Scalable Uncertainty Management ISBN: 3642043879 ISBN-13(EAN): 9783642043871 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume contains the papers presented at the Third International Conference on Scalable Uncertainty Management, SUM 2009, in Washington, DC, September 28-30, 2009.
Автор: Sergios Theodoridis Название: Pattern Recognition, ISBN: 1597492728 ISBN-13(EAN): 9781597492720 Издательство: Elsevier Science Рейтинг: Цена: 14483.00 р. Наличие на складе: Нет в наличии.
Описание: Considers classical and theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. This book provides an self-contained volume encapsulating this spectrum of information.
Автор: Kumar Название: Correlation Pattern Recognition ISBN: 0521153484 ISBN-13(EAN): 9780521153485 Издательство: Cambridge Academ Рейтинг: Цена: 8554.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a needed review of the diverse background material needed for correlation pattern recognition, developing the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. Includes case studies of interest, such as face and fingerprint recognition. For graduate students and practitioners.
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: Sergios Theodoridis Название: Introduction to Pattern Recognition: A Matlab Approach, ISBN: 0123744865 ISBN-13(EAN): 9780123744869 Издательство: Elsevier Science Рейтинг: Цена: 5557.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An accompanying manual to "Theodoridis/Koutroumbas, Pattern Recognition", that includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.
Автор: Bishop, Christopher M. Название: Neural Networks for Pattern Recognition ISBN: 0198538642 ISBN-13(EAN): 9780198538646 Издательство: Oxford Academ Рейтинг: Цена: 14414.00 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: Lars Eld?n Название: Matrix Methods in Data Mining and Pattern Recognition ISBN: 0898716268 ISBN-13(EAN): 9780898716269 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Поставка под заказ.
Описание: Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.
Автор: Pradipta Maji; Sushmita Paul Название: Scalable Pattern Recognition Algorithms ISBN: 3319056298 ISBN-13(EAN): 9783319056296 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models.
Автор: Christoph Beierle; Alex Dekhtyar Название: Scalable Uncertainty Management ISBN: 3319235397 ISBN-13(EAN): 9783319235394 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The mysterious world of normal numbers.- Bayesian Networks.- Probabilistic Query Answering in the Bayesian Description Logic BEL.- The Complexity of Plate Probabilistic Models.- DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Model.- Probabilistic Models.- State Space Search with Stochastic Costs and Risk Aversion.- On the Impact of Junction-Tree Topology on Weighted Model Counting.- A System for Probabilistic Inductive Answer Set Programming.- Towards Large-Scale Probabilistic OBDA.- Reasoning over Linear Probabilistic Knowledge Bases with Priorities.- Intelligent Data Analytics.- Evenness-based reasoning with logical proportions applied to classification.- Multivariate Cluster-Based Discretization for Bayesian Network Structure Learning.- Modeling and Forecasting Time Series of Compositional Data: A Generalized Dirichlet Power Steady Model.- Linguistic and Graphical Explanation of a Cluster-based Data Structure.- Possibility Theory, Belief Functions and Transformations.- Probability-possibility transformations: Application to credal networks.- Planning in Partially Observable Domains with Fuzzy Epistemic States and Probabilistic Dynamics.- Propagation of Belief Functions in Singly-Connected Hybrid Directed Evidential Networks.- Uncertain logical gates in possibilistic networks. An application to human geography.- Argumentation.- Undercutting in argumentation systems.- Formalizing Explanatory Dialogues.- Towards a dual process cognitive model for argument evaluation.- Change in abstract bipolar argumentation systems.- On argumentation with purely defeasible rules.- Dealing with Inconsistency.- A possibilistic analysis of inconsistency.- First-Order Under-Approximations of Consistent Query Answers.- Using Rules of Thumb for Repairing Inconsistent Answer Set Program.- Applications.- Fuzzy XPath for the Automatic Search of Fuzzy Formulae Models.- ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution.- Matching uncertain identities against sparse knowledge.
Описание: The application and validation of agent-based models.- Methods, and technologies in a number of key application areas.- In day life and real world, energy and networks, human and trust, markets and bids, models and tools, negotiation and conversation, scalability and resources.
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