最小误差熵准则的学习理论方法
Learning Theory Approach to Minimum Error Entropy Criterion
摘要：
我们考虑了最小误差熵准则和回归环境中的经验风险最小化学习算法。提出了一种学习理论方法，并给出了该算法的逼近能力和假设空间容量的显式误差界。对Renyi熵和Parzen窗函数的泛化误差进行了新的渐近分析，克服了经典最小二乘问题与经典最小二乘问题的本质区别所带来的技术困难.引入了与排序算法相关的半范数和对称化最小二乘误差。
英文摘要：
We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm in a regression setting. A learning theory approach is presented for this MEE algorithm and explicit error bounds are provided in terms of the approximation ability and capacity of the involved hypothesis space when the MEE scaling parameter is large. Novel asymptotic analysis is conducted for the generalization error associated with Renyi's entropy and a Parzen window function, to overcome technical difficulties arisen from the essential differences between the classical least squares problems and the MEE setting. A semi-norm and the involved symmetrized least squares error are introduced, which is related to some ranking algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
