D12-1117 training data for the supervised distance learning algorithm ( Section 3.1 ) . In
D11-1013 external hierarchies in semantic distance learning . We compared the performance
N13-1096 Online learning , a new trend in distance learning , provides numerous lectures
D11-1013 hierarchies to assist semantic distance learning . A distance metric w0 is learned
P05-1042 success in training HMMs . Edit distance learning could benefit from similar methods
D11-1013 external hierarchies in semantic distance learning . We investigated five sets of
D12-1117 , a user trains the supervised distance learning model via a taxonomy construction
P05-1042 Model Context and Memory in Edit Distance Learning : An Application to Pronunciation
N01-1020 Work Probabilistic string edit distance learning techniques have been studied
D11-1013 their discussions . For semantic distance learning , we collected 50 hierarchies
D11-1013 Overlap feature . 2.4.2 Semantic Distance Learning This section elaborates the learning
D11-1013 and then present the semantic distance learning algorithm that aims to find the
D11-1013 aspect identifica - tion , semantic distance learning , and aspect hierarchy generation
D11-1013 unigram feature . 2.4 Semantic Distance Learning Our aspect hierarchy generation
D12-1117 , we introduce a new semantic distance learning method ( Section 3.1 ) and extend
D12-1117 repositioned . Inspired by ME , we take a distance learning approach to deal with path consistency
D11-1013 4.3.4 Evaluations on Semantic Distance Learning In this section , we evaluated
D12-1117 browsing taxonomies . The supervised distance learning algorithm not only allows us
D12-1117 browsing taxonomies . A supervised distance learning algorithm not only allows us
W05-0108 rubrics of ( i ) workshops ( ii ) distance learning tools and ( iii ) coordination
hide detail