Machine Learning for Pattern Recognition (2nd Edition)

In the recent digital age, machine learning technology has made significant progress, revolutionizing applications in fields such as image recognition, speech processing, and natural language processing. These technologies have not only changed our daily lives but have also had a profound impact on...

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Bibliographische Detailangaben
Format:	Online
Sprache:	Englisch
Veröffentlicht:	MDPI - Multidisciplinary Digital Publishing Institute 2026
Schlagworte:	Deep learning algorithm Convolutional neural network Pipeline Corrosion Pinhole Finite element analysis Magnetic flux leakage signal Enzyme Peptide Binding Support vector machine Physicochemical features Sequential features Uncertainty estimation for embedding vectors Model uncertainty Data uncertainty Distributional uncertainty Re-identification Out-of-distribution Spectral response MSFA one-shot Deep learning Multispectral image Data augmentation Generative adversarial networks GANs Information security Voiceprint recognition UAV (unmanned aerial vehicle) UAV datasets Object detection Semantic segmentation Action recognition Event recognition Aerial Surveillance Padé approximation Moments Nonparametric probability estimation Centrifugal chiller system Reinforcement learning Deep learning neural network Hate speech detection Hate speech classification Two-layer approach Machine learning Electric vehicles Energy consumption Driving pattern recognition Representative driving cycles Optimization thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
Online-Zugang:	ONIX_20260416T142754_9783725863501_9
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Beschreibung
Zusammenfassung:	In the recent digital age, machine learning technology has made significant progress, revolutionizing applications in fields such as image recognition, speech processing, and natural language processing. These technologies have not only changed our daily lives but have also had a profound impact on medicine, finance, transportation, and other fields. However, pattern recognition, as an important branch of machine learning, still has many challenges and problems. This 2nd edition Reprint brings together contributions from leading experts in their fields. Each paper provides valuable insights into the latest trends, methods, and challenges in state-of-the-art applications of machine learning for pattern recognition. In addition, the studies in each paper not only showcase the latest advancements in machine learning algorithms but also discuss their successful applications and the challenges encountered in real-world scenarios. As Guest Editors, we are honored to present this 2nd edition Reprint, and we hope that readers, whether researchers, engineers, or students, will find inspiration and guidance in these papers as they explore the growing field of machine learning for pattern recognition. We express our gratitude to the authors for their outstanding contributions, to the reviewers for their critical evaluation, and to the Assistant Editor, Mr. Musea Wu, for his enthusiastic help. We are also sincerely grateful to our readers, whose curiosity and enthusiasm continue to drive innovation in this exciting field.

Machine Learning for Pattern Recognition (2nd Edition)

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