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98
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ICMLA
2008
15 years 3 months ago
Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous
Using decision trees that split on randomly selected attributes is one way to increase the diversity within an ensemble of decision trees. Another approach increases diversity by ...
Michael Gashler, Christophe G. Giraud-Carrier, Ton...
160
Voted
APPROX
2008
Springer
245views Algorithms» more  APPROX 2008»
15 years 3 months ago
Approximating Optimal Binary Decision Trees
Abstract. We give a (ln n + 1)-approximation for the decision tree (DT) problem. An instance of DT is a set of m binary tests T = (T1, . . . , Tm) and a set of n items X = (X1, . ....
Micah Adler, Brent Heeringa
91
Voted
ICML
2005
IEEE
16 years 2 months ago
Multi-instance tree learning
We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing multi-instance tree lea...
Hendrik Blockeel, David Page, Ashwin Srinivasan
ICML
2005
IEEE
16 years 2 months ago
Generalized skewing for functions with continuous and nominal attributes
This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary v...
Soumya Ray, David Page
TOG
2008
103views more  TOG 2008»
15 years 1 months ago
Sketch-based tree modeling using Markov random field
In this paper, we describe a new system for converting a user's freehand sketch of a tree into a full 3D model that is both complex and realistic-looking. Our system does thi...
Xuejin Chen, Boris Neubert, Ying-Qing Xu, Oliver D...