Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in General Games

Autor: Hassan, Ismail, Oommen, B. John, Yazidi, Anis

Artificial barriers in Learning Automata (LA) is a powerful and yet under-explored concept although it was first proposed in the 1980s. Introducing artificial non-absorbing barriers makes the LA schemes resilient to being trapped in absorbing barrier

Externí odkaz: http://arxiv.org/abs/2203.15780

Self Organizing Maps Whose Topologies Can Be Learned With Adaptive Binary Search Trees Using Conditional Rotations

Autor: Astudillo, César A., Oommen, B. John

Publikováno v: C\'esar A. Astudillo and B. John Oommen. Self Organizing Maps Whose Topologies Can Be Learned With Adaptive Binary Search Trees Using Conditional Rotations. Pattern Recognition, 47(1), 2014

Numerous variants of Self-Organizing Maps (SOMs) have been proposed in the literature, including those which also possess an underlying structure, and in some cases, this structure itself can be defined by the user Although the concepts of growing th

Externí odkaz: http://arxiv.org/abs/1506.02750

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On the classification of dynamical data streams using novel “Anti-Bayesian” techniques

Autor: Hammer, Hugo Lewi, Yazidi, Anis, Oommen, B. John

Publikováno v: In Pattern Recognition April 2018 76:108-124

On enhancing the object migration automaton using the Pursuit paradigm

Autor: Shirvani, Abdolreza, Oommen, B. John

Publikováno v: In Journal of Computational Science January 2018 24:329-342