… as decision trees and neural networks, instance-based learning algorithms do not create
an abstraction from specific instances. Rather, they simply store all the data, and at query time …

… Another area not considered in this review is the empirical evaluation of learning
algorithms for probabilistic networks. Empirical evaluation of learning algorithm is fraught with …

… In this article, a survey of the most important neural network learning models is … neural
networks and their learning paradigms is presented. Section 3 discusses the models of networks …

… networks research contained in this book is a family of Efficient LEarning Algorithms for Neural
Networks (… existing learning algorithms. Chapter 3 presents the development of efficient …

… learning approaches are simpler than unsupervised learning approaches. This paper reviews
the supervised learning approaches which are widely used in data … learning in networks of …

… Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of …
network learning algorithms. We introduce a software package for learning module networks, …

… Our work discusses machine learning algorithms based … data and the process of building
optimal learning trees is NP-complete [40]. 3) Neural networks (NNs): This learning algorithm …

… The importance of the proper learning algorithm was emphasized, because with advanced
learning algorithms, we can train those networks that cannot be trained with simple algorithms…

… Kenji Doya, Recurrent Networks … This article reviews the learning algorithms for training
recurrent networks. There are three major frameworks of learning: supervised learning based on …

… learning algorithm based on biological neurons which laid the foundation of Artificial Neural
Network [4, 5… In this section, we focus on some popular machine learning algorithms from the …