'Feature selection' presentation slideshows

Answer Extraction

Answer Extraction. Ling573 NLP Systems and Applications May 19, 2011. Roadmap . Noisy-channel Question-Answering Answer selection by reranking Redundancy-based Answer Selection . Noisy Channel QA. Employed for speech, POS tagging, MT, summ , etc Intuition:

959 views • 85 slides

Deep learning

2.45k views • 78 slides

Problem

Text Mining, Information and Fact Extraction Part 2: Machine Learning Techniques Marie-Francine Moens Department of Computer Science Katholieke Universiteit Leuven, Belgium sien.moens@cs.kuleuven.be. Problem.

749 views • 62 slides

Hyeonsoo , Kang

670 views • 53 slides

Object Category Detection: Sliding Windows

03/29/11. Object Category Detection: Sliding Windows. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Administrative . Remember to e-mail project descriptions (by Thurs) and talk to Ian or me (by Fri) Clarifications about the homework?.

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Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models

Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models . Yuval Marton Ph.D. Dissertation Defense Department of Linguistics University of Maryland. Dissertation Theme.

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Supervised and Unsupervised learning for Natural language processing

Supervised and Unsupervised learning for Natural language processing. Manaal Faruqui Language Technologies Institute SCS, CMU. Natural Language Processing. +. Linguistics. Computer Science. Natural Language Processing. But Why ? I nability to handle large amount of data

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Attributes for deeper supervision in learning: Two examples

Attributes for deeper supervision in learning: Two examples. Annotator rationales for visual recognition Active learning with objects and attributes. Slide credit: Kristen Grauman. Complex visual recognition tasks.

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Lecture 5: Causality and Feature Selection

Lecture 5: Causality and Feature Selection. Isabelle Guyon isabelle@clopinet.com. Variable/feature selection. Y. X. Remove features X i to improve (or least degrade) prediction of Y. What can go wrong?. Guyon-Aliferis-Elisseeff, 2007. What can go wrong?. X 2. Y. Y. X 2. X 1. X 1.

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Graph-of-word and TW-IDF: New Approach to Ad Hoc IR

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Minimum Redundancy and Maximum Relevance Feature Selection

Minimum Redundancy and Maximum Relevance Feature Selection. Hang Xiao. Background. Feature a feature is an individual measurable heuristic property of a phenomenon being observed In character recognition: horizontal and vertical profiles, number of internal holes, stroke detection

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Final review

Final review . LING572 Fei Xia Week 10: 03/13/08. Topics covered. Supervised learning: six algorithms kNN , NB: training and decoding DT, TBL: training and decoding (with binary features) MaxEnt : training (GIS) and decoding SVM: decoding Semi-supervised learning:

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Simonetta Montemagni * , Martijn Wieling + , Bob de Jonge + and John Nerbonne +

364 views • 21 slides

A Structured Model for Joint Learning of Argument Roles and Predicate Senses

A Structured Model for Joint Learning of Argument Roles and Predicate Senses. Yotaro Watanabe Masayuki Asahara Yuji Matsumoto. Tohoku University Nara Institute of Science and Technology. ACL 2010 Uppsala, Sweden July 12, 2010.

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Biomarker and Classifier Selection in Diverse Genetic Datasets

Biomarker and Classifier Selection in Diverse Genetic Datasets. University Of Connecticut 1 Department of Computer Science and Engineering 2 Department of Molecular and Cell Biology. James Lindsay 1 Ed Hemphill 2 Chih Lee 1 Ion Mandoiu 1 Craig Nelson 2.

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NIPS 2001 Workshop on Feature/Variable Selection Isabelle Guyon BIOwulf Technologies

NIPS 2001 Workshop on Feature/Variable Selection Isabelle Guyon BIOwulf Technologies. Schedule. 7:30-8:00: Welcome and introduction to the problem of feature/variable selection - Isabelle Guyon - 8:00-8:20 a.m. Dimensionality Reduction via Sparse Support Vector Machines - Jinbo Bi,

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Generalized Model Selection For Unsupervised Learning in High Dimension

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Hydroinformatics : Data Mining in Hydrology

Hydroinformatics : Data Mining in Hydrology . IIHR Seminar (December 3, 2010 ) Evan Roz. UNESCO-IHE, Delft, Dr. Solomatine. Hydroinformatics t echniques were adopted from computational intelligence (CI)/intelligent systems/machine learning hydroinformatics

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Appendix. LP SVM formulation

Appendix. LP SVM formulation. F rom E.Pranckeviciene and R.Somorjai, Liknon feature selection for Microarrays, in F.Masulli, S.Mitra, and G.Pasi(Eds.):WILF 2007, LNAI 4578, 580-587, 2007. LP SVM formulation. Primal. LP SVM formulation. Dual. LP SVM formulation. Optimality conditions.

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Introduction

EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning. Shuang Wang , 1 Xiaoqian Jiang, 1 Yuan Wu, 1 Lijuan Cui, 2 and Samuel Cheng 2 , Lucila Ohno- Machado 1.

167 views • 1 slides