AMALEA: Advances in Machine Learning
Prof.-Dr. George A. Tsihrintzis
University of Pireaus
Background and Goals
There are many real-world problems of such high complexity that traditional scientific approaches, based on physical and statistical modeling of the data generation mechanism, do not succeed in addressing them efficiently. The cause of inefficiencies often lies in multidimensionality, nonlinearities, chaotic phenomena and the presence of a plethora of degrees of freedom and unknown parameters in the mechanism that generates data. As a result, loss of information that is crucial to solve a problem is inherent in the data generation process itself, making a traditional mathematical solution intractable.
At the same time, however, biological systems have evolved to address similar problems in efficient ways. In nature, we observe abundant examples of high level intelligence arising via inter-networking components of only elementary intelligence:
Biological neural networks, i.e., networks of interconnected biological neurons in the nervous system of most multi-cellular animals, are capable of learning, memorizing and recognizing patterns in signals such as images, sounds, smells or odors.
Ants exhibit a collective, decentralized, and self-organized intelligence that allows them to discover the shortest route to food in very efficient ways.
Similar ideas are true with regards to immune systems, social networks, financial and business networks, or graphs.
Topics of Interests
biological and artificial neural networks
biological and artificial immune networks
biological and artificial swarm intelligence
fuzzy and neuro-fuzzy systems
deep belief networks/ deep learning approaches
advances in statistical learning
optimization methods in knowledge engineering
Call for Papers
Professor-Dr. George A. Tsihrintzis, University of Piraeus
Dr. Aristomenis Lampropoulos, National Documentation Center (NDC)/ National Hellenic Research Foundation (NHRF)
Dr. Dionysios Sotiropoulos, Athens University of Economics and Business