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作 者:束俊 孟德宇[1,2] 徐宗本 Jun SHU;Deyu MENG;Zongben XU(School of Mathematics and Statistics,Xi'an Jiaotong University,Xi'an 710049,China;Macao Institute of Systems Engineering,Macao University of Science and Technology,Macao 999078,China)
机构地区:[1]西安交通大学数学与统计学院,西安710049 [2]澳门科技大学澳门系统工程研究所,澳门999078
出 处:《中国科学:信息科学》2020年第6期781-793,共13页Scientia Sinica(Informationis)
基 金:国家自然科学基金(批准号:61661166011,11690011,61603292,61721002,U1811461)资助项目。
摘 要:自步学习是近年来机器学习领域提出的一种启发于人和动物"由易到难"学习过程的学习机制.尽管自步学习已取得可喜的理论与应用进展,但是当前的自步学习算法仍存在超参数选择的瓶颈问题.针对该问题当前主要采用一些启发式的手工设计方法或者交叉验证方法,然而此类方法效率很低,缺乏理论性指导,难以推广应用到广泛的实践问题中.针对这一挑战性问题,本文提出一种基于元学习机理的自步学习算法,该方法能使自步学习中涉及的超参数以数据驱动的方式自动习得,从而大大减弱了自步学习的这一核心问题.特别地,我们针对3种典型的自步学习实现格式,将所提元学习策略实质性嵌入,通过回归和分类实验验证了所提算法的准确性和泛化性,特别验证了相比于传统超参设置方法的显著优越性.Self-paced learning(SPL)is a learning regime,inspired by human and animal learning processes,that gradually incorporates simple to more complex samples into a training dataset.Recently,SPL has seen significant research progress.However,current SPL algorithms still have critical limitations,such as how to determine the age hyper-parameters(especially the age parameters).Some heuristic strategies based on cross-validation have been designed.In addition,setting these parameters manually has been proposed.However,such strategies are very inefficient,not supported by theoretical evidence,and are very difficult to apply generally in practice.To address these issues,we propose a meta-learning regime for adaptively learning age parameters involved in SPL.Three types of typical SPL algorithms are integrated into the proposed regime,and their accuracy and generalization capability are substantiated through regression and classification experiments,and compared to conventional SPL paradigms that do not include adaptive age parameter tuning.
关 键 词:自步学习 元学习 样本加权 噪声标记下学习 超参数选择
分 类 号:TP181[自动化与计算机技术—控制理论与控制工程]
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