Knowledge Discovery Conducted in the Areas of Machine Learning by High Performance Computing Using Evaluation of Learning Algorithms

The last decade has seen considerable growth in interest in Artificial Intelligence and Machine Learning. In the broadest sense, these fields aim to ‘learn something useful’ about the environment within which the organism operates. How gathered information is processed leads to the development of algorithms, how to process high dimensional data and deal with uncertainty. In the early stages of research in Machine Learning and related areas, similar techniques were discovered in relatively isolated research communities. Whilst not all techniques have a natural description in terms of probability theory, many do, and it is the framework of Graphical Models (a marriage between graph and probability theory) that has enabled the understanding and transference of ideas from statistical physics, statistics, machine learning and information theory. To this extent, it is now reasonable to expect that machine learning researchers are familiar with the basics of statistical modelling techniques. we apply different machine learning (ML) techniques for building objective models, that permit to automatically assess the image quality in agreement with human visual perception. The six ML methods proposed are discriminant analysis, k-nearest neighbors, artificial neural network, non-linear regression, decision tree and fuzzy logic. Both the stability and the robustness of designed models are evaluated by using Monte-Carlo cross-validation approach (MCCV). The simulation results demonstrate that fuzzy logic model provides the best prediction accuracy.