Metoda mejnih parov za učenje umetnih nevronskih mrež : doktorska disertacija

slovenian

doktorska dela

2013

Univerza v Mariboru, Fakulteta za elektrotehniko računalništvo in informatiko

Thesis describes a new method of machine learning - Border Pairs Method. The method described is intended for teaching Multi-Layer Perceptron - Feed-Forwarded Artificial Neural Network which is used for recognition or classification. Initially the problems of machine learning are described, as well as a description of the Multi-Layer Perceptron Neural Network and a description of Backpropagation, of its classical teaching method with an emphasis on its weaknesses. Firstly, in the core of the thesis we analyze the properties of trained MLP. In doing so, we focus on the learning patterns nearthe border and we define the concept of the Border Pair. Furthermore, the analysis of the properties of the border pairs, which is the basis of a new method of the noise reduction, of the clustering and a new method of constructive learning follow. This learning can be offline, incremental, online and with gradually forgetting the old learning patterns (unlearning). Noise cancellation, clustering and learning have been tested by simulation on a computer. Towards the end, we used the established sets of learning data (thanks to the comparability of results), real learning data sets (as a proof of the usability) as well as synthetic ones (due to the adaptation of learningdata to our needs). The comparative analysis showed that the Border Pairs Method has some good characteristics. With it we have successfully reduced the noise, clustered the data, searched the features and classify thedata. Results show that the researched method is reliable, accurate, constructive and insensitive to noise and overfitting.

07.08.2023