| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Zesch, T. & Gurevych, I. | Wisdom of crowds versus wisdom of linguists - measuring the semantic relatedness of words. [BibTeX] |
2010 | Natural Language Engineering | article | URL |
BibTeX:
@article{journals/nle/ZeschG10,
author = {Zesch, Torsten and Gurevych, Iryna},
title = {Wisdom of crowds versus wisdom of linguists - measuring the semantic relatedness of words.},
journal = {Natural Language Engineering},
year = {2010},
volume = {16},
number = {1},
pages = {25-59},
url = {http://dblp.uni-trier.de/db/journals/nle/nle16.html#ZeschG10}
}
|
|||||
| Du, L., Buntine, W. L. & Jin, H. | Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document. [BibTeX] |
2010 | ICDM | inproceedings | URL |
BibTeX:
@inproceedings{conf/icdm/DuBJ10,
author = {Du, Lan and Buntine, Wray Lindsay and Jin, Huidong},
title = {Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document.},
booktitle = {ICDM},
publisher = {IEEE Computer Society},
year = {2010},
pages = {148-157},
url = {http://dblp.uni-trier.de/db/conf/icdm/icdm2010.html#DuBJ10}
}
|
|||||
| Levy, O. & Goldberg, Y. | Linguistic Regularities in Sparse and Explicit Word Representations. [BibTeX] |
2014 | CoNLL | inproceedings | URL |
BibTeX:
@inproceedings{conf/conll/LevyG14,
author = {Levy, Omer and Goldberg, Yoav},
title = {Linguistic Regularities in Sparse and Explicit Word Representations.},
booktitle = {CoNLL},
publisher = {ACL},
year = {2014},
pages = {171-180},
url = {http://dblp.uni-trier.de/db/conf/conll/conll2014.html#LevyG14}
}
|
|||||
| Yu, H.-F., Jain, P., Kar, P. & Dhillon, I. S. | Large-scale Multi-label Learning with Missing Labels | 2013 | misc | URL | |
| Abstract: The multi-label classification problem has generated significant interest in cent years. However, existing approaches do not adequately address two key allenges: (a) the ability to tackle problems with a large number (say llions) of labels, and (b) the ability to handle data with missing labels. In is paper, we directly address both these problems by studying the multi-label oblem in a generic empirical risk minimization (ERM) framework. Our amework, despite being simple, is surprisingly able to encompass several cent label-compression based methods which can be derived as special cases of r method. To optimize the ERM problem, we develop techniques that exploit the ructure of specific loss functions - such as the squared loss function - to fer efficient algorithms. We further show that our learning framework admits rmal excess risk bounds even in the presence of missing labels. Our risk unds are tight and demonstrate better generalization performance for low-rank omoting trace-norm regularization when compared to (rank insensitive) obenius norm regularization. Finally, we present extensive empirical results a variety of benchmark datasets and show that our methods perform gnificantly better than existing label compression based methods and can ale up to very large datasets such as the Wikipedia dataset. |
|||||
BibTeX:
@misc{yu2013largescale,
author = {Yu, Hsiang-Fu and Jain, Prateek and Kar, Purushottam and Dhillon, Inderjit S.},
title = {Large-scale Multi-label Learning with Missing Labels},
year = {2013},
note = {cite arxiv:1307.5101},
url = {http://arxiv.org/abs/1307.5101}
}
|
|||||
| Mirowski, P., Ranzato, M. & LeCun, Y. | Dynamic Auto-Encoders for Semantic Indexing [BibTeX] |
2010 | inproceedings | URL | |
BibTeX:
@inproceedings{noauthororeditor,
author = {Mirowski, Piotr and Ranzato, Marc'Aurelio and LeCun, Yann},
title = {Dynamic Auto-Encoders for Semantic Indexing},
year = {2010},
url = {http://yann.lecun.com/exdb/publis/pdf/mirowski-nipsdl-10.pdf}
}
|
|||||
| Karampatziakis, N. & Mineiro, P. | Discriminative Features via Generalized Eigenvectors | 2013 | misc | URL | |
| Abstract: Representing examples in a way that is compatible with the underlying assifier can greatly enhance the performance of a learning system. In this per we investigate scalable techniques for inducing discriminative features taking advantage of simple second order structure in the data. We focus on lticlass classification and show that features extracted from the generalized genvectors of the class conditional second moments lead to classifiers with cellent empirical performance. Moreover, these features have attractive eoretical properties, such as inducing representations that are invariant to near transformations of the input. We evaluate classifiers built from these atures on three different tasks, obtaining state of the art results. |
|||||
BibTeX:
@misc{karampatziakis2013discriminative,
author = {Karampatziakis, Nikos and Mineiro, Paul},
title = {Discriminative Features via Generalized Eigenvectors},
year = {2013},
note = {cite arxiv:1310.1934},
url = {http://arxiv.org/abs/1310.1934}
}
|
|||||
| Kohlschütter, C., Fankhauser, P. & Nejdl, W. | Boilerplate Detection using Shallow Text Features [BibTeX] |
2010 | Proc. of 3rd ACM International Conference on Web Search and Data Mining New York City, NY USA (WSDM 2010). | inproceedings | |
BibTeX:
@inproceedings{conf/wsdm/KohlschutterFN10,
author = {Kohlschütter, Christian and Fankhauser, Peter and Nejdl, Wolfgang},
title = {Boilerplate Detection using Shallow Text Features},
booktitle = {Proc. of 3rd ACM International Conference on Web Search and Data Mining New York City, NY USA (WSDM 2010).},
year = {2010}
}
|
|||||
Created by JabRef export filters on 28/04/2024 by the social publication management platform PUMA