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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 20 of 20 1 Distinctive feature fusion for recognition of Australian English consonants Trent W. Lewis; David M. W. Powers In: http://dl.dropboxusercontent.com/u/27743223/200809-Interspeech2008-DistinctiveFeatureFusion.pdf (2008) BASE Show details 2 D.M.W.: Verb similarity on the taxonomy of WordNet Dongqiang Yang; David M. W. Powers In: http://david.wardpowers.info/Research/AI/papers/200601-GWC-VerbSimWN.pdf (2006) BASE Show details 3 A Basis for Compact Distributional Extraction Abstract David M. W. Powers; German Ai In: http://members.dodo.com.au/~powers/Research/AI/papers/199208-THINK-CDE.pdf (2003) BASE Show details 4 Audio-Visual Speech Recognition using Red Exclusion and Neural Networks Trent W. Lewis; David M. W. Powers In: http://www.jrpit.flinders.edu.au/confpapers/CRPITV4Lewis.pdf (2003) BASE Show details 5 AudioVisual Speech Recognition using Red Exclusion and Neural Networks Trent W. Lewis; David M. W. Powers In: http://ws.acs.org.au/jrpit/JRPIT35.1.41.pdf (2003) BASE Show details 6 Audio-Visual Speech Recognition using Red Exclusion and Neural Networks Trent W. Lewis; David M. W. Powers In: http://www.infoeng.flinders.edu.au/papers/20030001.pdf (2003) BASE Show details 7 Lip Feature Extraction Using Red Exclusion Trent W. Lewis; David M. W. Powers In: http://www.jrpit.flinders.edu.au/confpapers/CRPITV2Lewis.pdf (2001) BASE Show details 8 Lip Feature Extraction Using Red Exclusion Trent W. Lewis; David M. W. Powers In: http://www.cs.flinders.edu.au/Research/AI/papers/200001-VIP-AVSR.pdf (2001) BASE Show details 9 Learning and Application of Differential Grammars David M. W. Powers In: ftp://ai.ist.flinders.edu.au/pub/ai/papers/199703-CoNLL-DG.ps.gz (1997) BASE Show details 10 System design methodology Dongqiang Yang; David M. W. Powers In: http://david.wardpowers.info/Research/AI/papers/200603-ACL%2BCoLing-WordSimWN.pdf (1995) BASE Show details 11 A Multifaceted Investigation into Speech Reading Trent W. Lewis; David M. W. Powers In: http://members.dodo.com.au/~powers/Research/AI/papers/200108-HIS-MISR.pdf BASE Show details 12 An empirical evaluation David M. W. Powers; An Empirical Evaluation In: http://www.infoeng.flinders.edu.au/papers/19970003.pdf BASE Show details 13 Word Sense Disambiguation using lexical cohesion in the context Dongqiang Yang; David M. W. Powers In: http://acl.ldc.upenn.edu/P/P06/P06-2119.pdf BASE Show details 14 An Empirical Investigation into Grammatically Constrained Contexts in Predicting Distributional Similarity Dongqiang Yang; David M. W. Powers In: http://dl.dropboxusercontent.com/u/27743223/200713-ALTW-DistributionalSimilarity.pdf BASE Show details 15 An Empirical Investigation into Grammatically Constrained Contexts in Predicting Distributional Similarity Dongqiang Yang; David M. W. Powers In: http://www.alta.asn.au/events/altw2007/cdrom/pdf/ALTA2007_17.pdf BASE Show details 16 Smoothing, Normalization, Correction and Reduction David M. W. Powers In: http://dl.dropboxusercontent.com/u/27743223/200504-drft-Normalization+LSI+SVD+Katz+TFIDF.pdf Abstract: Eigenvector techniques are very common across computer science, artificial intelligence, data mining, computational linguistics, eResearch and in particular are important for reducing the size of “Big Data ” and making it manageable. But the cost of applying the standard PCA, LSI or SVD techniques is very large is huge unless we can operate on sparse data. On the other hand, the techniques require for their proper operation normalization or standardization that destroys sparsity. We explore in this paper, from both a theoretical and a practical perspective, techniques that can allow retention of sparsity and avoidance of the hugely expensive eigenvector reduction, including a straightforward introduction to SVD as well as a recommended procedure for standardizing data while maximizing sparsity. URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.602.4863 http://dl.dropboxusercontent.com/u/27743223/200504-drft-Normalization+LSI+SVD+Katz+TFIDF.pdf BASE Hide details 17 What Unsupervised Learning Tells Us About Language Models David Powers Artificial; David M. W. Powers In: ftp://ai.ist.flinders.edu.au/pub/ai/papers/199606-TASL-WULTAL.ps.gz BASE Show details 18 Smoothing, Normalization, Correction and Reduction David M. W. Powers In: http://david.wardpowers.info/Research/AI/papers/200504-drft-Normalization%2BLSI%2BSVD%2BKatz%2BTFIDF.pdf BASE Show details 19 ROBOT BABIES David M. W. Powers In: http://david.wardpowers.info/Research/AI/papers/200106-inv-RBE.pdf BASE Show details 20 Robot babies: what can they teach us about language acquisition? David M. W. Powers In: http://david.wardpowers.info/Research/AI/papers/200004-inv-RBE.pdf BASE Show details

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