Self-Paced Boost Learning for Classification / 1932
Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang
Effectiveness and robustness are two essential aspects of supervised learning studies. For effective learning, ensemble methods are developed to build a strong effective model from ensemble of weak models. For robust learning, self-paced learning (SPL) is proposed to learn in a self-controlled pace from easy samples to complex ones. Motivated by simultaneously enhancing the learning effectiveness and robustness, we propose a unified framework, Self-Paced Boost Learning (SPBL). With an adaptive from-easy-to-hard pace in boosting process, SPBL asymptotically guides the model to focus more on the insufficiently learned samples with higher reliability. Via a max-margin boosting optimization with self-paced sample selection, SPBL is capable of capturing the intrinsic inter-class discriminative patterns while ensuring the reliability of the samples involved in learning. We formulate SPBL as a fully-corrective optimization for classification. The experiments on several real-world datasets show the superiority of SPBL in terms of both effectiveness and robustness.