Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Balancing the n-Cube: A Census of Colorings
Journal of Algebraic Combinatorics: An International Journal
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Sequential skewing: an improved skewing algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Skewing: an efficient alternative to lookahead for decision tree induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions
The Journal of Machine Learning Research
Hi-index | 0.00 |
We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the presence of irrelevant variables. We prove tha, in an idealized setting, for any function and choice of skew parameters, skewing finds relevant variables with probability 1. We present experiments exploring how different parameter choices affect the success of skewing in empirical settings. Finally, we analyze a variant of skewing called Sequential Skewing.