Posterior Distribution Learning (PDL): A Novel Supervised Learning Framework

  • Enmei Tu
  • Jie Yang
  • Zhenghong Jia
  • Nicola Kasabov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8834)

Abstract

In order to obtain a robust supervised model with good generalization ability, traditional supervised learning method has to be trained with sufficient well labeled and uniformly distributed samples. However, in many real applications, the cost of labeled samples is generally very expensive. How to make use of ample easily available unlabeled samples to remedy the insufficiency of labeled samples to train a supervised model is of great interest and practical significance. In this paper we propose a new supervised learning framework, Posterior Distribution Learning (PDL), which could train a robust supervised model with very a few labeled samples by including those unlabeled samples into training stage. Experimental results on both synthetic and real world data sets are presented to demonstrate the effectiveness of the proposed framework.

Keywords

distribution learning nonlinear regression manifold classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Enmei Tu
    • 1
  • Jie Yang
    • 1
  • Zhenghong Jia
    • 2
  • Nicola Kasabov
    • 3
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityChina
  2. 2.School of Information Science and EngineeringXinjiang UniversityUrumqiChina
  3. 3.The Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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