Equilibrium properties of off-line LVQ
نویسندگان
چکیده
The statistical physics analysis of offline learning is applied to cost function based learning vector quantization (LVQ) schemes. Typical learning behavior is obtained from a model with data drawn from high dimensional Gaussian mixtures and a system of two or three competing prototypes. The analytic approach becomes exact in the limit of high training temperature. We study two cost function related LVQ algorithms and the influence of an appropriate weight decay. In our findings, learning from mistakes (LFM) achieves poor generalization ability, while a limiting case of generalized LVQ (GLVQ), termed LVQ+/-, displays much better performance with a properly chosen weight decay.
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