SLIT: Designing Complexity Penalty for Classification and Regression Trees Using the SRM Principle

  • Zhou Yang
  • Wenjie Zhu
  • Liang Ji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


The statistical learning theory has formulated the Structural Risk Minimization (SRM) principle, based upon the functional form of risk bound on the generalization performance of a learning machine. This paper addresses the application of this formula, which is equivalent to a complexity penalty, to model selection tasks for decision trees, whereas the quantization of the machine capacity for decision trees is estimated using an empirical approach. Experimental results show that, for either classification or regression problems, this novel strategy of decision tree pruning performs better than alternative methods. We name classification and regression trees pruned by virtue of this methodology as Statistical Learning Intelligent Trees (SLIT).


Regression Tree Empirical Risk Statistical Learn Theory Tree Pruning Pruning Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhou Yang
    • 1
  • Wenjie Zhu
    • 2
  • Liang Ji
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems & Institute of Information Processing, Dept. of AutomationTsinghua UniversityBeijingChina
  2. 2.Dept. of Statistics and Actuarial SciencesThe University of Hong KongHong Kong S.A.R.

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