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F Schleif

Publications and source records attributed to F Schleif.

2 recordsLinked to original sources

Fuzzy classification by fuzzy labeled neural gas.

We extend the neural gas for supervised fuzzy classification. In this way we are able to learn crisp as well as fuzzy clustering, given labeled data. Based on the neural gas cost function, we propose three different ways to incorporate the additional class information into the learning algorithm. We demonstrate the effect on the location of the prototypes and the classification accuracy. Further, we show that relevance learning can be easily included.

Algorithms↗

Comparison of relevance learning vector quantization with other metric adaptive classification methods.

The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability of metric adaptation but based on different concepts are considered in comparison to variants of relevance learning vector quantization. We compare these methods with respect to their theoretical motivation and we demonstrate the differences of their behavior for several real world data sets.

Artificial Intelligence↗