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DropConnect (the generalization of Dropout) is a very simple regularization technique that was introduced a few years ago and has become extremely popular because of its simplicity and effectiveness. In this thesis, a suitable architecture for applying DropConnect to Learning Vector Quantization networks is proposed along with a reference implementation and experimental results. Inmany classification tasks, the uncertainty of themodel is a vital piece of information for experts. Methods to extract the uncertainty and stability using DropConnect are also proposed and the corresponding experimental results are documented.
Soft Learning Vector Quantisation (SLVQ) andRobust Soft Learning Vector Quantisation (RSLVQ) are supervised data classification methods, that have been applied successfully to real world classification problems. The performance of SLVQ and RSLVQ, however, reduces, when they are applied tomore complicated classification problems. In this thesis, we have introducedmodi-fications to SLVQand RSLVQ, in order to havemore capable versions of them. A few possibilities to modify SLVQ and RSLVQ are considered, some of them are not successful enough and they have been included for the sake of completeness. The fruits of the thesis are plenty, including Tangent Soft Learning Vector Quantisation-Strong (TSLVQ-S), together with its more stable version Tangent Robust Soft Learning Vector Quantisation-Strong (TRSLVQ-S), Attraction Soft Learning Vector Quantisation (ASLVQ) and Grassmannian Soft Learning Vector Quantisation (GSLVQ).