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An Aggressive Margin-Based Algorithm for Incremental Learning

  • JuiHsi Fu
  • SingLing Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)

Abstract

In incremental learning, the classification model is incrementally updated using the small datasets. Different with existing methods, our approach updates the current classifier according to each sample in the dataset, respectively. The classifier is updated by adjusting more than the margin of each sample. Then the new classifier is generated by carefully analyzing classifier adjustments caused for labeled samples. Additionally the new classifier shall correct prediction mistakes of the previous classifier as many as possible. In details, we formulate simple constrained optimization problems and then the updated classifier is the solution derived using Lagrange multipliers. In our experiments, 13 real-world dataset are used to present the effectiveness of the proposed approach. The experimental results are shown that our update strategy is able to adjust the classifier properly. And it is also shown that the proposed incremental learning approach is suitable to be applied for the requirement of frequently adjusting the existing classifiers.

Keywords

Incremental Learning Margin-based Approaches Passive-Aggressive (PA) Algorithm Period Datasets Classifier Adjustment 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • JuiHsi Fu
    • 1
  • SingLing Lee
    • 1
  1. 1.National Chung Cheng UniversityChiayiTaiwan, R.O.C.

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