---
title: "Self-Paced Boost Learning for Classification"
authors:
  - "Te Pi"
  - "Xi Li"
  - "Zhongfei Zhang"
  - "Deyu Meng"
  - "Fei Wu"
  - "Jun Xiao"
  - "Yueting Zhuang"
doi: ""
url: "https://ijcai.org/proceedings/2016/276"
pdf: "https://ijcai.org/proceedings/2016/0276.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1932-1937"
track: ""
keywords:
---

# Self-Paced Boost Learning for Classification

Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1932-1937.

## Abstract

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.