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This project explores several Machine Learning methods to predict movie genres based on plot summaries.
Generalization of backpropagation with application to a recurrent gas market model
Paul J Werbos · 1988
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Untersuchungen zu dynamischen neuronalen netzen
Sepp Hochreiter · 1991
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Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
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A desicion-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1995
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Arcing the edge
Leo Breiman · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Learning to forget: Continual prediction with lstm
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins · 1999
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Boostexter: A boosting-based system for text categorization
Robert E Schapire and Yoram Singer · 2000
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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A kernel method for multi-labelled classification
André Elisseeff and Jason Weston · 2002
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Stochastic gradient boosting
Jerome H Friedman · 2002
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Multilabel neural networks with applications to functional genomics and text categorization
Min-Ling Zhang and Zhi-Hua Zhou · 2006
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Classifying movie scripts by genre with a memm using nlp-based features, 2008
Alex Blackstock and Matt Spitz · 2008
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Movies genres classification by synopsis, 2011
Ka wing Ho · 2011
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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https://code.google.com/archive/p/word2vec/
Pretrained google news vectors · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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ftp://ftp.fu-berlin.de/pub/misc/movies/database/
Imdb data
Cited in the paper.
http://xgboost.readthedocs.io/en/latest/build.html
Xgboost python package
Cited in the paper.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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A multinomial probabilistic model for movie genre predictions
Eric Makita and Artem Lenskiy · 2016
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Recurrent dropout without memory loss
Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth · 2016
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