Multivariate class labeling in Robust Soft LVQ
نویسندگان
چکیده
We introduce a generalization of Robust Soft Learning Vector Quantization (RSLVQ). This algorithm for nearest prototype classification is derived from an explicit cost function and follows the dynamics of a stochastic gradient ascent. We generalize the RSLVQ cost function with respect to vectorial class labels: Probabilistic LVQ (PLVQ) allows to realize multivariate class memberships for prototypes and training samples, and the prototype labels can be learned from the data during training. We present experiments to demonstrate the new algorithm in practice.
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