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ECML
2007
Springer
15 years 10 months ago
Structure Learning of Probabilistic Relational Models from Incomplete Relational Data
Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn...
Xiao-Lin Li, Zhi-Hua Zhou
FOCS
2003
IEEE
15 years 9 months ago
Learning DNF from Random Walks
We consider a model of learning Boolean functions from examples generated by a uniform random walk on {0, 1}n . We give a polynomial time algorithm for learning decision trees and...
Nader H. Bshouty, Elchanan Mossel, Ryan O'Donnell,...
ECML
1993
Springer
15 years 8 months ago
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
This paper describes a genetic learning system called SIA, which learns attributes based rules from a set of preclassified examples. Examples may be described with a variable numbe...
Gilles Venturini
139
Voted
KDD
2006
ACM
113views Data Mining» more  KDD 2006»
16 years 4 months ago
A new efficient probabilistic model for mining labeled ordered trees
Mining frequent patterns is a general and important issue in data mining. Complex and unstructured (or semi-structured) datasets have appeared in major data mining applications, i...
Kosuke Hashimoto, Kiyoko F. Aoki-Kinoshita, Nobuhi...
ISMB
1994
15 years 5 months ago
Predicting Location and Structure Of beta-Sheet Regions Using Stochastic Tree Grammars
We describe and demonstrate the effectiveness of a method of predicting protein secondary structures, sheet regions in particular, using a class of stochastic tree grammars as rep...
Hiroshi Mamitsuka, Naoki Abe