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Detecting, Measuring and Representing Vagueness in Ontologies
Technology Detecting, Measuring and Representing Vagueness in Ontologies

1. Detecting, Analyzing and Representing Vagueness inOntologies for Facilitating Data ReuseDr. Panos AlexopoulosSemantic Applications Research ManageriSOCO S.A.University…

Effective Named Entity Recognition for Idiosyncratic Web Collections
Science Effective Named Entity Recognition for Idiosyncratic Web Collections

1. Effective Named Entity Recognition for Idiosyncratic Web Collections Roman Prokofyev, Gianluca Demartini, Philippe Cudre-Mauroux eXascale Infolab, University of Fribourg,…

Classifying text NLTK Chapter 6. Chapter 6 topics How can we identify particular features of language data that are salient for classifying it? How can.
Documents Classifying text NLTK Chapter 6. Chapter 6 topics How can we identify particular features of...

Slide 1 Classifying text NLTK Chapter 6 Slide 2 Chapter 6 topics How can we identify particular features of language data that are salient for classifying it? How can we…

What is the Best Multi-Stage Architecture for Object Recognition? Ruiwen Wu [1] Jarrett, Kevin, et al. "What is the best multi-stage architecture for object.
Documents What is the Best Multi-Stage Architecture for Object Recognition? Ruiwen Wu [1] Jarrett, Kevin, et.....

Slide 1 What is the Best Multi-Stage Architecture for Object Recognition? Ruiwen Wu [1] Jarrett, Kevin, et al. "What is the best multi-stage architecture for object…

C-SVDDNet: An Effective Single-Layer Network for Unsupervised Feature Learning Dong Wang and Xiaoyang Tan.
Documents C-SVDDNet: An Effective Single-Layer Network for Unsupervised Feature Learning Dong Wang and...

Slide 1 C-SVDDNet: An Effective Single-Layer Network for Unsupervised Feature Learning Dong Wang and Xiaoyang Tan Slide 2 I. INTRODUCTION Learning good feature representation…

Better Together: Large Monolingual, Bilingual and Multimodal Corpora in Natural Language Processing Fall, 2011 Shane Bergsma Johns Hopkins University.
Documents Better Together: Large Monolingual, Bilingual and Multimodal Corpora in Natural Language Processing....

Slide 1 Better Together: Large Monolingual, Bilingual and Multimodal Corpora in Natural Language Processing Fall, 2011 Shane Bergsma Johns Hopkins University Slide 2 2 Research…

On the use of data mining for imputation
Documents On the use of data mining for imputation

Production of a short-term indicator of households budget 26 Donald B. Rubin, "Multiple Imputation After 18+ Years", JASA, vol. 91, no. 434, June 1996 "…Judging…

Shane Bergsma Johns Hopkins University
Documents Shane Bergsma Johns Hopkins University

Slide 1 Better Together: Large Monolingual, Bilingual and Multimodal Corpora in Natural Language Processing Fall, 2011 Shane Bergsma Johns Hopkins University 1 Research Vision…

AdaBoost 1. Classifier Simplest classifier 2 3 Adaboost: Agenda (Adaptive Boosting, R. Scharpire, Y. Freund, ICML, 1996): Supervised classifier Assembling.
Documents AdaBoost 1. Classifier Simplest classifier 2 3 Adaboost: Agenda (Adaptive Boosting, R. Scharpire, Y....

Slide 1 AdaBoost 1 Slide 2 Classifier Simplest classifier 2 Slide 3 3 Slide 4 Adaboost: Agenda (Adaptive Boosting, R. Scharpire, Y. Freund, ICML, 1996): Supervised classifier…

Clustering Detecting margin regions
Documents Clustering Detecting margin regions

Max-margin Clustering: Detecting Margins from Projections of Points on Lines Raghuraman Gopalan1, and Jagan Sankaranarayanan2 1Center for Automation Research, University…

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