Kernel robust soft learning vector quantization

摘要: Prototype-based classification schemes offer very intuitive and flexible classifiers with the benefit of easy interpretability results scalability model complexity. Recent prototype-based models such as robust soft learning vector quantization (RSLVQ) have a solid mathematical foundation rule decision boundaries in terms probabilistic corresponding likelihood optimization. In its original form, they can be used for standard Euclidean vectors only. this contribution, we extend RSLVQ towards kernelized version which any positive semidefinite data matrix. We demonstrate superior performance technique, kernel RSLVQ, variety benchmarks where competitive or even to state-of-the-art support machines are obtained.