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An Advanced Least Squares Twin Multi-class Classification Support Vector Machine for Few-Shot Classification

  • Yu Li
  • Zhonggeng Liu
  • Huadong PanEmail author
  • Jun Yin
  • Xingming Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11936)

Abstract

In classification tasks, deep learning methods yield high performance. However, owing to lack of enough annotated data, deep learning methods often underperformed. Therefore, we propose an advance version of least squares twin multi-class classification support vector machine (ALST-KSVC) which leads to low computational complexity and comparable accuracy based on LST-KSVC for few-shot classification. In ALST-KSVC, we modified optimization problems to construct a new “1-versus-1-versus-1” structure, proposed a new decision function, and constructed smaller number of classifiers than our baseline LST-KSVC. We empirically demonstrate that the proposed method has better classification accuracy than LST-KSVC. Especially, ALST-KSVC achieves the state-of-the-art performance on MNIST, USPS, Amazon, Caltech image datasets and Iris, Teaching evaluation, Balance, Wine, Transfusion UCI datasets.

Keywords

Few-shot Multi-classes classification Support vector machine 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yu Li
    • 1
  • Zhonggeng Liu
    • 1
  • Huadong Pan
    • 1
    Email author
  • Jun Yin
    • 1
  • Xingming Zhang
    • 1
  1. 1.Advanced Research Institute of Zhejiang Dahua Technology Co. Ltd.HangzhouChina

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