Compact coding for hyperplane classifiers in heterogeneous environment

Hao Shao, Bin Tong, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Transfer learning techniques have witnessed a significant development in real applications where the knowledge from previous tasks are required to reduce the high cost of inquiring the labeled information for the target task. However, how to avoid negative transfer which happens due to different distributions of tasks in heterogeneous environment is still a open problem. In order to handle this kind of issue, we propose a Compact Coding method for Hyperplane Classifiers (CCHC) under a two-level framework in inductive transfer learning setting. Unlike traditional methods, we measure the similarities among tasks from the macro level perspective through minimum encoding. Particularly speaking, the degree of the similarity is represented by the relevant code length of the class boundary of each source task with respect to the target task. In addition, informative parts of the source tasks are adaptively selected in the micro level viewpoint to make the choice of the specific source task more accurate. Extensive experiments show the effectiveness of our algorithm in terms of the classification accuracy in both UCI and text data sets.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, Proceedings
Number of pages16
EditionPART 3
Publication statusPublished - 2011
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2011 - Athens, Greece
Duration: Sept 5 2011Sept 9 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6913 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2011

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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