Measuring the VC-dimension of a learning machine
Neural Computation
Machine Learning
Machine Learning
Pessimistic decision tree pruning based Continuous-time
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Comparison of model selection for regression
Neural Computation
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Measuring the VC-Dimension Using Optimized Experimental Design
Neural Computation
Model complexity control for regression using VC generalization bounds
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
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).