TY - JOUR
T1 - Learning Decision Trees with Stochastic Linear Classifiers
AU - Jurgenson, Tom
AU - Mansour, Yishay
N1 - Publisher Copyright:
© 2018 T. Jurgenson & Y. Mansour.
PY - 2018
Y1 - 2018
N2 - In this work we propose a top-down decision tree learning algorithm with a class of linear classifiers called stochastic linear classifiers as the internal nodes’ hypothesis class. To this end, we derive efficient algorithms for minimizing the Gini index for this class for each internal node, although the problem is non-convex. Moreover, the proposed algorithm has a theoretical guarantee under the weak stochastic hypothesis assumption.
AB - In this work we propose a top-down decision tree learning algorithm with a class of linear classifiers called stochastic linear classifiers as the internal nodes’ hypothesis class. To this end, we derive efficient algorithms for minimizing the Gini index for this class for each internal node, although the problem is non-convex. Moreover, the proposed algorithm has a theoretical guarantee under the weak stochastic hypothesis assumption.
UR - http://www.scopus.com/inward/record.url?scp=85070470501&partnerID=8YFLogxK
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AN - SCOPUS:85070470501
SN - 2640-3498
VL - 83
SP - 489
EP - 528
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
T2 - 29th International Conference on Algorithmic Learning Theory, ALT 2018
Y2 - 7 April 2018 through 9 April 2018
ER -