N06-1042 rules are generated by a novel decision list learning algorithm using supervised training
N03-2025 on this training corpus , the Decision List Learning algorithm -LSB- Segal & Etzioni
P03-1043 This training corpus supports the Decision List Learning which learns homogeneous rules
W04-3104 , Naïve Bayes learning , Decision List learning , Support Vector Machine ) .
W04-3104 using Mesh Headings along with Decision list learning or Support Vector machine has
W02-1036 stage classifier , we employ a decision list learning method . Experimental evaluation
A00-2015 learning method , we employ the decision list learning method of Yarowsky ( 1994 ) .
P03-2031 carried out the experiment with the decision list learning method and the result is shown
C00-2102 how to incorporate them into the decision list learning framework will be described in
W02-1036 Learning Algorithm We apply a simple decision list learning method to the task of learning
C04-1058 is to ask why methods such as decision list learning ( Rivest , 1987 ) as well as
P00-1036 might present difficulties for decision list learning were it not for the independence
P03-2031 corpus . We separately trained the decision list learning features using the automatically
W04-3104 F-measure ( i.e. , 90.5 % ) . Decision list learning along with stemmed words in titles
W04-3104 including Naïve Bayes learning , Decision List learning and Support Vector Machine for
W03-1121 addition to a heuristic based on decision list learning , they also presented a boosting-like
A00-2015 sentences of EDR corpus . 3.4 Decision List Learning of Dependency Preference of Subordinate
A00-2015 then model the evidence of the decision list learning method as any possible pair (
W02-1003 the traditional probabilistic decision list learning algorithm -- equivalent to Sorted
C00-2102 lnarginal probabil - ity . 4.2 Decision List Learning for Chunking/Tagging Named Entities
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