Online Passive-Aggressive Total-Error-Rate Minimization

Autor:	Jang, Se-In
Rok vydání:	2020
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.01771 Zobrazit plný text záznamu View this record from Arxiv