Process Mining Techniques for Pattern Recognition, Yadav, Vikash
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 1493938436 ISBN-13(EAN): 9781493938438 Издательство: Springer Рейтинг: Цена: 9970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Leskovec Jure Название: Mining of Massive Datasets ISBN: 1108476341 ISBN-13(EAN): 9781108476348 Издательство: Cambridge Academ Рейтинг: Цена: 10771.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Автор: Guorong Wu; Pierrick Coup?; Yiqiang Zhan; Brent Mu Название: Patch-Based Techniques in Medical Imaging ISBN: 3319281933 ISBN-13(EAN): 9783319281933 Издательство: Springer Рейтинг: Цена: 5854.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A Multi-level Canonical Correlation Analysis Scheme for Standard-dose PET Image Estimation.- Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity from High-Resolution Image.- Automatic Hippocampus Labeling Using the Hierarchy of Sub-Region Random Forests.- Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis.- Improving Accuracy of Automatic Hippocampus Segmentation in Routine MRI by Features Learned from Ultra-high Field MRI.- Dual-Layer l1-Graph Embedding for Semi-Supervised Image Labeling.- Automatic Liver Tumor Segmentation in Follow-up CT Studies Using Convolutional Neural Network.- Block-based Statistics for Robust Non-Parametric Morphometry.- Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-based Edge Detection.- Efficient Lung Cancer Cell Detection with Deep Convolutional Neural Network.- An Effective Approach for Robust Lung Cancer Cell Detection.- Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation.- Hippocampus Segmentation through Distance Field Fusion.- Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compress Sensing.- Fast Regions-of-Interest Detection in Whole Slide Histopathology Images.- Reliability Guided Forward and Backward Patch-based Method for Multi-atlas Segmentation.- Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptor.- Multi-Atlas Segmentation using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression.- A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images.- 3D MRI Denoising using Rough Set Theory and Kernel Embedding Method.- A Novel Cell Orientation Congruence Descriptor for Superpixel based Epithelium Segmentation in Endometrial Histology Images.- Patch-based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques.- Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph.- Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework.- Efficient Multi-Scale Patch-based Segmentation.
Описание: This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques.Presents pattern recognition and the computational intelligence using Matlab;Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly;Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.
Описание: The artificial intelligence subset machine learning has become a popular technique in professional fields as many are finding new ways to apply this trending technology into their everyday practices. Two fields that have majorly benefited from this are pattern recognition and information security. The ability of these intelligent algorithms to learn complex patterns from data and attain new performance techniques has created a wide variety of uses and applications within the data security industry. There is a need for research on the specific uses machine learning methods have within these fields, along with future perspectives.
Machine Learning Techniques for Pattern Recognition and Information Security is a collection of innovative research on the current impact of machine learning methods within data security as well as its various applications and newfound challenges. While highlighting topics including anomaly detection systems, biometrics, and intrusion management, this book is ideally designed for industrial experts, researchers, IT professionals, network developers, policymakers, computer scientists, educators, and students seeking current research on implementing machine learning tactics to enhance the performance of information security.
Описание: This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts.
Описание: The artificial intelligence subset machine learning has become a popular technique in professional fields as many are finding new ways to apply this trending technology into their everyday practices. Two fields that have majorly benefited from this are pattern recognition and information security. The ability of these intelligent algorithms to learn complex patterns from data and attain new performance techniques has created a wide variety of uses and applications within the data security industry. There is a need for research on the specific uses machine learning methods have within these fields, along with future perspectives.
Machine Learning Techniques for Pattern Recognition and Information Security is a collection of innovative research on the current impact of machine learning methods within data security as well as its various applications and newfound challenges. While highlighting topics including anomaly detection systems, biometrics, and intrusion management, this book is ideally designed for industrial experts, researchers, IT professionals, network developers, policymakers, computer scientists, educators, and students seeking current research on implementing machine learning tactics to enhance the performance of information security.
Автор: Martha Refugio Ortiz-Posadas Название: Pattern Recognition Techniques Applied to Biomedical Problems ISBN: 3030380203 ISBN-13(EAN): 9783030380205 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers pattern recognition techniques applied to various areas of biomedicine, including disease diagnosis and prognosis, and several problems of classification, with a special focus on-but not limited to-pattern recognition modeling of biomedical signals and images.
Автор: Ortiz-Posadas Martha Refugio Название: Pattern Recognition Techniques Applied to Biomedical Problems ISBN: 3030380238 ISBN-13(EAN): 9783030380236 Издательство: Springer Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers pattern recognition techniques applied to various areas of biomedicine, including disease diagnosis and prognosis, and several problems of classification, with a special focus on-but not limited to-pattern recognition modeling of biomedical signals and images.
Описание: This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature.
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