Passive-Aggressive online learning with nonlinear embeddings

PATTERN RECOGNITION

卷:79

Article

Jorge, Javier¹  Paredes, Roberto¹ 

[1] Univ Politecn Valencia, Pattern Recognit & Human Language Technol Res Ctr, Campus Vera,Camino Vera S-N, E-46022 Valencia, Spain

关键词: Online learning;    Nonlinear functions;    Passive-Aggressive;    Binary classification;    Nonlinear embedding;   

DOI  :  10.1016/j.patcog.2018.01.019

来源: Elsevier

【 摘 要 】

Nowadays, there is an increasing demand for machine learning techniques which can deal with problems where the instances are produced as a stream or in real time. In these scenarios, online learning is able to learn a model from data that comes continuously. The adaptability, efficiency and scalability of online learning techniques have been gaining interest last years with the increasing amount of data generated every day. In this paper, we propose a novel binary classification approach based on nonlinear mapping functions under an online learning framework. The non-convex optimization problem that arises is split into three different convex problems that are solved by means of Passive-Aggressive Online Learning. We evaluate both the adaptability and generalization of our model through several experiments comparing with the state of the art techniques. We improve significantly the results in several datasets widely used previously by the online learning community. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free