  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.
