Malzahn Dörthe, Opper Manfred
Neural Computing Research Group, School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, United Kingdom.
Phys Rev Lett. 2002 Sep 2;89(10):108302. doi: 10.1103/PhysRevLett.89.108302. Epub 2002 Aug 19.
Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone.
通过使用变分技术，我们将从随机示例中学习的统计物理方法进行了推广，使其适用于实际数据。我们通过计算仅基于训练数据的泛化误差的近似估计量，证明了我们方法的有效性和相关性。
