Active Learning for Optimal Generalization in Trigonometric Polynomial Models
Active Learning for Optimal Generalization in Trigonometric Polynomial Models
复制标题
三角多项式模型中最优泛化的主动学习
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
H. Ogawa
中科院分区：
文献类型：
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作者：
Masashi Sugiyama;H. Ogawa
本文考虑了主动学习问题，给出了最优泛化能力的一个充分必要条件。利用伪正交基的性质，阐明了实现最优泛化能力的机理。我们还表明，该条件不仅提供了最佳的泛化能力，而且降低了计算学习结果函数的计算复杂度和所需的内存。基于最优性条件，给出了三角多项式模型最优样本点的设计方法。最后，通过计算机仿真验证了主动学习方法的有效性。
In this paper, we consider the problem of active learning, and give a necessary and sufficient condition of sample points for the optimal generalization capability. By utilizing the properties of pseudo orthogonal bases, we clarify the mechanism of achieving the optimal generalization capability. We also show that the condition does not only provide the optimal generalization capability but also reduces the computational complexity and memory required for calculating learning result functions. Based on the optimality condition, we give design methods of optimal sample points for trigonometric polynomial models. Finally, the effectiveness of the proposed active learning method is demonstrated through computer simulations.