Type-2 Fuzzy Graphical Models for Pattern Recognition, Jia Zeng; Zhi-Qiang Liu
Автор: Liu, Zhi-Qiang Название: Type-2 Fuzzy Graphical Models for Pattern Recognition ISBN: 3662446898 ISBN-13(EAN): 9783662446898 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition.
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: 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: 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: Gernot A. Fink Название: Markov Models for Pattern Recognition ISBN: 1447171330 ISBN-13(EAN): 9781447171331 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book places the formalism of Markov chain and hidden Markov models at the very center of its examination of current pattern recognition systems, demonstrating how the models can be used in a range of different applications.
Автор: M. Jorge Cardoso; Ivor Simpson; Tal Arbel; Doina P Название: Bayesian and grAphical Models for Biomedical Imaging ISBN: 3319122886 ISBN-13(EAN): 9783319122885 Издательство: Springer Рейтинг: Цена: 5590.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
N3 Bias Field Correction Explained as a Bayesian Modeling Method.- A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging.- Optimal Joint Segmentation and Tracking of Escherichia Coli in the Mother Machine.- Physiologically Informed Bayesian Analysis of ASL fMRI Data.- Bone Reposition Planning for Corrective Surgery Using Statistical Shape Model: Assessment of Differential Geometrical Features.- An Inference Language for Imaging.- An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation.- Learning Imaging Biomarker Trajectories from Noisy Alzheimer's Disease Data Using a Bayesian Multilevel Model.- Four Neuroimaging Questions that P-Values Cannot Answer (and Bayesian Analysis Can).- Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies.- A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions.
Описание: Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases.- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases.- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images.- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images.- Inferring Disease Status by non-Parametric Probabilistic Embedding.- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images.- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study.- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker.- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation.- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images.- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features.- Representation Learning for Cross-Modality Classification.- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound.- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images.- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data.- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields.- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data.- Non-local Graph-based Regularization for Deformable Image Registration.- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation.
Автор: M.I. Jordan Название: Learning in Graphical Models ISBN: 9401061041 ISBN-13(EAN): 9789401061049 Издательство: Springer Рейтинг: Цена: 41925.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Proceedings of the NATO Advanced Study Institute, Ettore Maiorana Centre, Erice, Italy, September 27-October 7, 1996
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.
Автор: Sergios Theodoridis Название: Pattern Recognition, ISBN: 1597492728 ISBN-13(EAN): 9781597492720 Издательство: Elsevier Science Рейтинг: Цена: 14483.00 р. Наличие на складе: Поставка под заказ.
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: Fink, Gernot A. Название: Markov models for pattern recognition ISBN: 1447163079 ISBN-13(EAN): 9781447163077 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book places the formalism of Markov chain and hidden Markov models at the very center of its examination of current pattern recognition systems, demonstrating how the models can be used in a range of different applications.
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