Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Mendoza
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 9978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Luis Rueda; Domingo Mery; Josef Kittler Название: Progress in Pattern Recognition, Image Analysis and Applications ISBN: 354076724X ISBN-13(EAN): 9783540767244 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007, held in Valparaiso, Chile, November 13-16, 2007. This book includes papers that cover research and mathematical methods for pattern recognition, image analysis, and applications in such diverse areas as computer vision, industry, and health.
Автор: Jos? Ruiz-Shulcloper; Walter Kropatsch Название: Progress in Pattern Recognition, Image Analysis and Applications ISBN: 3540859195 ISBN-13(EAN): 9783540859192 Издательство: Springer Рейтинг: Цена: 16070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, held in Havana, Cuba, in September 2008. This title also includes papers that are organized in sections on signal analysis for characterization and filtering, analysis of shape and texture, analysis of speech and language, and data mining.
Автор: Le Vuong Et Al Название: Face Processing And Applications To Distance Learning ISBN: 9814733024 ISBN-13(EAN): 9789814733021 Издательство: World Scientific Publishing Рейтинг: Цена: 10296.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This special compendium provides a concise and unified vision of facial image processing. It addresses a collection of state-of-the-art techniques, covering the most important areas for facial biometrics and behavior analysis. These techniques also converge to serve an emerging practical application of interactive distance learning.
Readers will get a broad picture of the fundamental science of the field and technical details that make the research interesting. Moreover, the intellectual investigation motivated by the demand of real-life application will make this volume an inspiring read for current and prospective researchers and engineers in the fields of computer vision, machine learning and image processing.
Описание: This book constitutes the refereed proceedings of the 20th Iberoamerican Congress on Pattern Recognition, CIARP 2015, held in Montevideo, Uruguay, in November 2015. The 95 papers presented were carefully reviewed and selected from 185 submissions. The papers are organized in topical sections on applications on pattern recognition;
Описание: This book constitutes the refereed conference proceedings of the 24rd Iberoamerican Congress on Pattern Recognition, CIARP 2019, held in Havana, Cuba, in October 2019. The 70 papers presented were carefully reviewed and selected from 128 submissions. Mathematical Theory of Pattern Recognition;
Описание: This book constitutes the refereed proceedings of the 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014, held in Puerto Vallarta, Jalisco, Mexico, in November 2014. The papers are organized in topical sections on image coding, processing and analysis; pattern recognition and machine learning; neural networks for pattern recognition;
Описание: Machine Learning.- Data Augmentation via Variational Auto-Encoders.- LSTM-based Multi-scale Model for WindSpeed Forecasting.-Using deep learning to classify class imbalanced gene-expression microarrays datasets.-A data representation approach to support imbalanced data classification beased in TWSVM.- Improving regression models by dissimilarity representation of bio-chemical data.- A Comparative Study on Unsupervised Domain Adaption for Coffee Crop Mapping.- Analytical Comparison of Histogram Distance Measures.- Gaussian processes regression with multiple annotors.- Linear projection learned from hybrid CKA for enhancing distance-based classifiers.- Evaluation of Bag-of-Word performance for time series classification using discriminative SIFT-based mid-level representations.- Color classification methods for perennial weed detection in cereal crops.- Hierarchical graph-based segmentation in detection of object-related regions.- Dealing with heterogeneous Google Earth images on Building Area Detection task.- Evaluation of scale-aware realignments of hierarchical image segmentation.
Автор: Diego Alexander Tibaduiza Burgos, Maribel Anaya Ve Название: Pattern Recognition Applications in Engineering ISBN: 1799818403 ISBN-13(EAN): 9781799818403 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 23839.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The implementation of data and information analysis has become a trending solution within multiple professions. New tools and approaches are continually being developed within data analysis to further solve the challenges that come with professional strategy. Pattern recognition is an innovative method that provides comparison techniques and defines new characteristics within the information acquisition process. Despite its recent trend, a considerable amount of research regarding pattern recognition and its various strategies is lacking. Pattern Recognition Applications in Engineering is an essential reference source that discusses various strategies of pattern recognition algorithms within industrial and research applications and provides examples of results in different professional areas including electronics, computation, and health monitoring. Featuring research on topics such as condition monitoring, data normalization, and bio-inspired developments, this book is ideally designed for analysts; researchers; civil, mechanical, and electronic engineers; computing scientists; chemists; academicians; and students.
Автор: Alberto Sanfeliu; Jos? F. Mart?nez Trinidad; Jes?s Название: Progress in Pattern Recognition, Image Analysis and Applications ISBN: 3540235272 ISBN-13(EAN): 9783540235279 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 9th Iberoamerican Congress on Pattern Recognition, CIARP 2004, held in Puebla, Mexico in October 2004. This work addresses issues in pattern recognition, image analysis, and computer vision, including applications in areas like robotics, entertainment, space exploration, data mining, and others.
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.
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