… and key definitions, paradigms, and results are … of learning, with particular emphasis on
machine learning. More specifically, we focus on statistical learning and the two main paradigms …

… representation, we have sparse learning and deep learning paradigms, both more recent …
hybrid learning paradigm constructed using mixed generative and discriminative learning. …

… machine learning and bio-inspiring optimization. Besides research articles and expository
papers on theory and algorithms of machine learning … Machine Learning in Feature Selection …

… In this article, we will introduce the basic concept of machine learning algorithms and the …
, unsupervised, and reinforcement learning. Machine learning can be widely used in modeling …

… of 10 machine learning paradigms in the form of an interrelation diagram, which we call the
learning-paradigm … Thereafter, we discuss seven modern machine learning paradigms and …

… the understanding of dominant paradigms of machine learning. Machine learning can solve
… It can be inferred that machine learning as a variety of models is used to learn patterns from …

… Ensemble learning is a type of machine learning paradigm that consists of a number of
learners called base learners (also known as weak learners) who are trained and combined to …

… important in the discovery of paradigms for artificial intelligence and machine learning. In …
of paradigms for machine learning in this volume. In terms of the yield from a machine learning …

Harmful algal blooms, which are considered a serious environmental problem nowadays,
occur in coastal waters in many parts of the world. They cause acute ecological damage and …

… Learning Analytics as a Machine Learning Paradigm. Special emphasis is placed on addressing
the four Learning … the general title MACHINE LEARNING PARADIGMS and follows two …