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ECML
2003
Springer
15 years 11 months ago
Improving the AUC of Probabilistic Estimation Trees
Abstract. In this work we investigate several issues in order to improve the performance of probabilistic estimation trees (PETs). First, we derive a new probability smoothing that...
César Ferri, Peter A. Flach, José He...
142
Voted
AAAI
2006
15 years 7 months ago
When a Decision Tree Learner Has Plenty of Time
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
Saher Esmeir, Shaul Markovitch
EMNLP
2006
15 years 7 months ago
Better Informed Training of Latent Syntactic Features
We study unsupervised methods for learning refinements of the nonterminals in a treebank. Following Matsuzaki et al. (2005) and Prescher (2005), we may for example split NP withou...
Markus Dreyer, Jason Eisner
EMNLP
2009
15 years 4 months ago
Refining Grammars for Parsing with Hierarchical Semantic Knowledge
This paper proposes a novel method to refine the grammars in parsing by utilizing semantic knowledge from HowNet. Based on the hierarchical state-split approach, which can refine ...
Xiaojun Lin, Yang Fan, Meng Zhang, Xihong Wu, Huis...
146
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GLOBECOM
2006
IEEE
16 years 6 days ago
Self-Adaptive Ad-Hoc/Sensor Network Routing with Attractor-Selection
Abstract— In this paper we propose MARAS, a biologicallyinspired method for routing in a mobile ad-hoc/sensor network environment. We assume that all nodes have no explicit knowl...
Kenji Leibnitz, Naoki Wakamiya, Masayuki Murata