论文标题
在线被动攻击总率最小化
Online Passive-Aggressive Total-Error-Rate Minimization
论文作者
论文摘要
我们提供了一种新的在线学习算法，该算法利用在线被动攻击性学习（PA）和总误差最小化（TER）进行二进制分类。 PA学习不仅建立了较大的保证金培训，而且还建立了处理不可分割数据的能力。另一方面，TER学习可以最大程度地减少基于分类错误的目标函数。我们提出了一种在线Pater算法，结合了这些有用的属性。此外，我们还提出了一种加权Pater算法，以提高应对数据不平衡问题的能力。实验结果表明，所提出的PATER算法比实际数据集中现有的最新在线学习算法在效率和有效性方面取得更好的性能。
We provide a new online learning algorithm which utilizes online passive-aggressive learning (PA) and total-error-rate minimization (TER) for binary classification. The PA learning establishes not only large margin training but also the capacity to handle non-separable data. The TER learning on the other hand minimizes an approximated classification error based objective function. We propose an online PATER algorithm which combines those useful properties. In addition, we also present a weighted PATER algorithm to improve the ability to cope with data imbalance problems. Experimental results demonstrate that the proposed PATER algorithms achieves better performances in terms of efficiency and effectiveness than the existing state-of-the-art online learning algorithms in real-world data sets.
