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SDM
2009
SIAM
112views Data Mining» more  SDM 2009»
14 years 2 months ago
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.
Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. In this paper, we evaluat...
Frederik Janssen, Johannes Fürnkranz
ACL
2001
13 years 7 months ago
Japanese Named Entity Recognition based on a Simple Rule Generator and Decision Tree Learning
Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classified into categories such as person, organization, loc...
Hideki Isozaki
AI
1998
Springer
13 years 10 months ago
ELEM2: A Learning System for More Accurate Classifications
We present ELEM2, a new method for inducing classification rules from a set of examples. The method employs several new strategies in the induction and classification processes to ...
Aijun An, Nick Cercone
ICML
2004
IEEE
14 years 6 months ago
Learning first-order rules from data with multiple parts: applications on mining chemical compound data
Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorl...
Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numa...
EVOW
2005
Springer
13 years 11 months ago
Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework
A hyperheuristic is a high level procedure which searches over a space of low level heuristics rather than directly over the space of problem solutions. The sequence of low level h...
Konstantin Chakhlevitch, Peter I. Cowling