Applied Machine Learning Ⅱ

This reprint focuses on applications of machine learning models in a diverse range of fields and problems. It reports substantive results on a wide range of learning methods; discusses the conceptualization of problems, data representation, feature engineering, and machine learning models; undertake...

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Format:	Online
Sprache:	Englisch
Veröffentlicht:	MDPI - Multidisciplinary Digital Publishing Institute 2024
Schlagworte:	activity recognition machine learning wearable sensors spinal cord injury telerehabilitation consumer analysis cost-sensitive learning imbalanced dataset over-the-top training data update demand forecasting smart manufacturing artificial intelligence supply chain agility digital twin path planning unmanned aerial vehicles neural networks evolutionary algorithms visible light communications (VLC) gesture recognition (GR) human-computer interaction (HCI) human activity recognition (HAR) machine learning (ML) neural network long short-term memory (LSTM) photo-diode (PD) road traffic accidents data analysis missing data dimensionality reduction textual analysis emotional tone financial crisis early warning DNA computer biochips queue automata type IIB endonucleases COVID-19 prediction medical diagnosis optimisation Generative Adversarial Networks Wasserstein GAN regression multi-output regression multi-modal distributions computer vision object detection human detection convolutional neural networks natural language controlled natural language natural language processing privacy policies social networks n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
Online-Zugang:	ONIX_20240514_9783725800735_341
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Beschreibung
Zusammenfassung:	This reprint focuses on applications of machine learning models in a diverse range of fields and problems. It reports substantive results on a wide range of learning methods; discusses the conceptualization of problems, data representation, feature engineering, and machine learning models; undertakes critical comparisons with existing techniques; and presents an interpretation of the results. This reprint strives not only to showcase the prolific applications of machine learning, but also to provide insights into the methodologies and interpretations that underpin these advancements.

Applied Machine Learning Ⅱ

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