An analysis of inference with the universum (2008)
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| Venue: | Advances in Neural Information Processing Systems 20 |
| Citations: | 8 - 0 self |
BibTeX
@INPROCEEDINGS{Sinz08ananalysis,
author = {Fabian H. Sinz and Olivier Chapelle and Alekh Agarwal and Bernhard Schölkopf},
title = {An analysis of inference with the universum},
booktitle = {Advances in Neural Information Processing Systems 20},
year = {2008},
pages = {1369--1376},
publisher = {MIT Press}
}
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Abstract
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative data, a third class of data is available, termed the Universum. We assay the behavior of the algorithm by establishing links with Fisher discriminant analysis and oriented PCA, as well as with an SVM in a projected subspace (or, equivalently, with a data-dependent reduced kernel). We also provide experimental results. 1
Citations
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| 27 | Probabilistic methods for support vector machines - Sollich |
| 20 | On the need for on-line learning in brain-computer interfaces - Millán - 2004 |
| 16 | Inference with the universum - Weston, Collobert, et al. - 2006 |
| 7 | Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of non-paralysed and completely paralysed subjects - Hill, Lal, et al. - 2006 |
| 6 | Transductive inference and semi-supervised learning,” in Semi-Supervised Learning - Vapnik - 2006 |
| 3 | Machine Learning Methods for Brain-Computer Interfaces - Lal - 2005 |
| 1 | Gaussian processes and the null-category noise model - Lawrence, Jordan - 2006 |
| 1 | A new support vector algorithm - Zhong, Fukushima |
