Bridging deep and multiple kernel learning: A review
T Wang, L Zhang, W Hu - Information Fusion, 2021 - Elsevier
Kernel methods and deep learning are two of the most currently remarkable machine
learning techniques that have achieved great success in many applications. Kernel methods …
learning techniques that have achieved great success in many applications. Kernel methods …
Positive and unlabeled learning algorithms and applications: A survey
This paper will address the Positive and Unlabeled learning problem (PU learning) and its
importance in the growing field of semi-supervised learning. In most real-world classification …
importance in the growing field of semi-supervised learning. In most real-world classification …
Solar array fault detection using neural networks
In this paper, we describe a Cyber-Physical system approach to fault detection in
Photovoltaic (PV) arrays. More specifically, we explore customized neural network …
Photovoltaic (PV) arrays. More specifically, we explore customized neural network …
[KÖNYV][B] Machine learning for solar array monitoring, optimization, and control
The efficiency of solar energy farms requires detailed analytics and information on each
panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar …
panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar …
Predicting miRNA–Disease Associations Through Deep Autoencoder With Multiple Kernel Learning
Determining microRNA (miRNA)–disease associations (MDAs) is an integral part in the
prevention, diagnosis, and treatment of complex diseases. However, wet experiments to …
prevention, diagnosis, and treatment of complex diseases. However, wet experiments to …
Deep ensemble machine for video classification
Video classification has been extensively researched in computer vision due to its wide
spread applications. However, it remains an outstanding task because of the great …
spread applications. However, it remains an outstanding task because of the great …