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SSPR
1998
Springer
13 years 12 months ago
Multi-interval Discretization Methods for Decision Tree Learning
Properly addressing the discretization process of continuos valued features is an important problem during decision tree learning. This paper describes four multi-interval discreti...
Petra Perner, Sascha Trautzsch
JALC
2007
95views more  JALC 2007»
13 years 7 months ago
Learning Regular Tree Languages from Correction and Equivalence Queries
Inspired by the results obtained in the string case, we present in this paper the extension of the correction queries to regular tree languages. Relying on Angluin’s and Sakakib...
Catalin Ionut Tîrnauca, Cristina Tîrna...
CAI
2007
Springer
14 years 1 months ago
Learning Deterministically Recognizable Tree Series - Revisited
Abstract. We generalize a learning algorithm originally devised for deterministic all-accepting weighted tree automata (wta) to the setting of arbitrary deterministic wta. The lear...
Andreas Maletti
KDD
2003
ACM
150views Data Mining» more  KDD 2003»
14 years 8 months ago
Learning relational probability trees
Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probabili...
Jennifer Neville, David Jensen, Lisa Friedland, Mi...
ICML
2006
IEEE
14 years 8 months ago
Kernelizing the output of tree-based methods
We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...