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Volumn , Issue , 2015, Pages 2100-2105

Bayesian optimization of text representations

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SENTIMENT ANALYSIS;

EID: 84959927890     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d15-1251     Document Type: Conference Paper
Times cited : (26)

References (30)
  • 1
    • 74549196844 scopus 로고    scopus 로고
    • The power of negative thinking: Exploiting label disagreement in the min-cut classification framework
    • Mohit Bansal, Clair Cardie, and Lillian Lee. 2008. The power of negative thinking: Exploiting label disagreement in the min-cut classification framework. In Proc. of COLING.
    • (2008) Proc. of COLING
    • Bansal, M.1    Cardie, C.2    Lee, L.3
  • 3
    • 85162384813 scopus 로고    scopus 로고
    • Algorithms for hyper-parameter optimization
    • James Bergstra, Remi Bardenet, Yoshua Bengio, and Balazs Kegl. 2011. Algorithms for hyper-parameter optimization. In NIPS.
    • (2011) NIPS
    • Bergstra, J.1    Bardenet, R.2    Bengio, Y.3    Kegl, B.4
  • 4
    • 84897558007 scopus 로고    scopus 로고
    • Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
    • James Bergstra, Daniel Yamins, and David Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Proc. of ICML.
    • (2013) Proc. of ICML.
    • Bergstra, J.1    Yamins, D.2    Cox, D.3
  • 5
    • 33750264215 scopus 로고    scopus 로고
    • Combined optimization of feature selection and algorithm parameters in machine learning of language
    • Walter Daelemans, Veronique Hoste, Fien De Meulder, and Bart Naudts. 2003. Combined optimization of feature selection and algorithm parameters in machine learning of language. In Proc. of ECML.
    • (2003) Proc. of ECML.
    • Daelemans, W.1    Hoste, V.2    De Meulder, F.3    Naudts, B.4
  • 6
    • 84867123033 scopus 로고    scopus 로고
    • Training restricted Boltzmann machines on word observations
    • George E. Dahl, Ryan P. Adams, and Hugo Larochelle. 2012. Training restricted Boltzmann machines on word observations. In Proc. of ICML.
    • (2012) Proc. of ICML.
    • Dahl, G.E.1    Adams, R.P.2    Larochelle, H.3
  • 8
    • 84856930049 scopus 로고    scopus 로고
    • Sequential model-based optimization for general algorithm configuration
    • Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. 2011. Sequential model-based optimization for general algorithm configuration. In Proc. of LION.
    • (2011) Proc. of LION.
    • Hutter, F.1    Hoos, H.H.2    Leyton-Brown, K.3
  • 9
    • 84960120039 scopus 로고    scopus 로고
    • Effective use of word order for text categorization with convolutional neural networks
    • Rie Johnson and Tong Zhang. 2015. Effective use of word order for text categorization with convolutional neural networks. In Proc. of NAACL.
    • (2015) Proc. of NAACL.
    • Johnson, R.1    Zhang, T.2
  • 10
    • 0035577808 scopus 로고    scopus 로고
    • A taxonomy of global optimization methods based on response surfaces
    • Donald R. Jones. 2001. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21:345-385.
    • (2001) Journal of Global Optimization , vol.21 , pp. 345-385
    • Jones, D.R.1
  • 11
    • 85142688646 scopus 로고
    • Newsweeder: Learning to filter netnews
    • Ken Lang. 1995. Newsweeder: Learning to filter netnews. In Proc. of ICML.
    • (1995) Proc. of ICML.
    • Lang, K.1
  • 12
    • 56449110012 scopus 로고    scopus 로고
    • Classification using discriminative restricted Boltzmann machines
    • Hugo Larochelle and Yoshua Bengio. 2008. Classification using discriminative restricted Boltzmann machines. In Proc. of ICML.
    • (2008) Proc. of ICML.
    • Larochelle, H.1    Bengio, Y.2
  • 13
    • 84919829999 scopus 로고    scopus 로고
    • Distributed representations of sentences and documents
    • Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proc. of ICML.
    • (2014) Proc. of ICML.
    • Le, Q.V.1    Mikolov, T.2
  • 16
    • 85015450140 scopus 로고    scopus 로고
    • Hidden factors and hidden topics: Understanding rating dimensions with review text
    • Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proc. of Rec Sys.
    • (2013) Proc. of Rec Sys.
    • McAuley, J.1    Leskovec, J.2
  • 19
    • 84869201485 scopus 로고    scopus 로고
    • Practical Bayesian optimization of machine learning algorithms
    • Jasper Snoek, Hugo Larrochelle, and Ryan P. Adams. 2012. Practical Bayesian optimization of machine learning algorithms. In NIPS.
    • (2012) NIPS
    • Snoek, J.1    Larrochelle, H.2    Adams, R.P.3
  • 20
    • 80053261327 scopus 로고    scopus 로고
    • Semi-supervised recursive autoencoders for predicting sentiment distributions
    • Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proc. of EMNLP.
    • (2011) Proc. of EMNLP.
    • Socher, R.1    Pennington, J.2    Huang, E.H.3    Ng, A.Y.4    Manning, C.D.5
  • 21
    • 84870715081 scopus 로고    scopus 로고
    • Semantic compositionality through recursive matrix-vector spaces
    • Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proc. of EMNLP.
    • (2012) Proc. of EMNLP.
    • Socher, R.1    Huval, B.2    Manning, C.D.3    Ng, A.Y.4
  • 23
    • 84937874740 scopus 로고    scopus 로고
    • Learning distributed representations for structured output prediction
    • Vivek Srikumar and Christopher D. Manning. 2014. Learning distributed representations for structured output prediction. In NIPS.
    • (2014) NIPS
    • Srikumar, V.1    Manning, C.D.2
  • 24
    • 77956501313 scopus 로고    scopus 로고
    • Gaussian process optimization in the bandit setting: No regret and experimental design
    • Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger. 2010. Gaussian process optimization in the bandit setting: No regret and experimental design. In Proc. of ICML.
    • (2010) Proc. of ICML.
    • Srinivas, N.1    Krause, A.2    Kakade, S.3    Seeger, M.4
  • 25
    • 84898939805 scopus 로고    scopus 로고
    • Multi-task Bayesian optimization
    • Kevin Swersky, Jasper Snoek, and Ryan P. Adams. 2013. Multi-task Bayesian optimization. In NIPS.
    • (2013) NIPS
    • Swersky, K.1    Snoek, J.2    Adams, R.P.3
  • 26
    • 80053357527 scopus 로고    scopus 로고
    • Get out the vote: Determining support or opposition from congressional floor-debate transcripts
    • Matt Thomas, Bo Pang, and Lilian Lee. 2006. Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In Proc. of EMNLP.
    • (2006) Proc. of EMNLP
    • Thomas, M.1    Pang, B.2    Lee, L.3
  • 27
    • 67650938640 scopus 로고    scopus 로고
    • An informational approach to the global optimization of expensive-to-evaluate functions
    • Julien Villemonteix, Emmanuel Vazquez, and Eric Walter. 2009. An informational approach to the global optimization of expensive-to-evaluate functions. Journal of Global Optimization, 44(4):509-534.
    • (2009) Journal of Global Optimization , vol.44 , Issue.4 , pp. 509-534
    • Villemonteix, J.1    Vazquez, E.2    Walter, E.3
  • 28
    • 84875872773 scopus 로고    scopus 로고
    • Baselines and bigrams: Simple, good sentiment and topic classification
    • Sida Wang and Christopher D. Manning. 2012. Baselines and bigrams: Simple, good sentiment and topic classification. In Proc. of ACL.
    • (2012) Proc. of ACL.
    • Wang, S.1    Manning, C.D.2
  • 29
    • 84255163690 scopus 로고    scopus 로고
    • Multi-level structured models for document sentiment classification
    • Ainur Yessenalina, Yisong Yue, and Claire Cardie. 2010. Multi-level structured models for document sentiment classification. In Proc. of EMNLP.
    • (2010) Proc. of EMNLP.
    • Yessenalina, A.1    Yue, Y.2    Cardie, C.3
  • 30
    • 84944206615 scopus 로고    scopus 로고
    • Efficient transfer learning method for automatic hyperparameter tuning
    • Dani Yogatama and Gideon Mann. 2014. Efficient transfer learning method for automatic hyperparameter tuning. In Proc. of AISTATS.
    • (2014) Proc. of AISTATS.
    • Yogatama, D.1    Mann, G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.