Yakoub Bazi

What is he best known for?

The fields of study Yakoub Bazi is best known for:

• Machine learning
• Artificial neural network
• Statistics

Contextual image classification, Thresholding and Aerial image are inherently bound to his Image (mathematics) studies. His Contextual image classification study typically links adjacent topics like Image (mathematics). His Classifier (UML) study falls within the topics of Domain adaptation and Margin classifier. His Margin classifier study frequently links to adjacent areas such as Classifier (UML). His Mathematical analysis research overlaps with other disciplines such as Domain (mathematical analysis) and Distribution (mathematics). In his research, Yakoub Bazi performs multidisciplinary study on Distribution (mathematics) and Mathematical analysis. As part of his Radial basis function and Extreme learning machine and Artificial neural network studies, he is studying Artificial neural network. Yakoub Bazi integrates many fields in his works, including Radial basis function and Support vector machine. Yakoub Bazi integrates Support vector machine with Feature extraction in his study.

His most cited work include:

• An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images (608 citations)
• Deep learning approach for active classification of electrocardiogram signals (437 citations)
• Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (434 citations)

What are the main themes of his work throughout his whole career to date

Yakoub Bazi is involved in relevant fields of research such as Artificial neural network and Support vector machine in the field of Machine learning. Artificial neural network and Machine learning are two areas of study in which Yakoub Bazi engages in interdisciplinary research. Yakoub Bazi integrates Artificial intelligence and Data mining in his studies. Computer vision connects with themes related to Image (mathematics) in his study. His research links Contextual image classification with Image (mathematics). His Contextual image classification study frequently draws parallels with other fields, such as Computer vision. Borrowing concepts from Deep learning, he weaves in ideas under Convolutional neural network. Yakoub Bazi carries out multidisciplinary research, doing studies in Deep learning and Convolutional neural network. His Classifier (UML) study typically links adjacent topics like Domain adaptation.

Yakoub Bazi most often published in these fields:

• Artificial intelligence (98.63%)
• Pattern recognition (psychology) (83.56%)
• Computer vision (46.58%)

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