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Author  |
Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Error-Correcting Output Codes Library | Type | Journal Article | ||
| Year | 2010 | Publication | Journal of Machine Learning Research | Abbreviated Journal | JMLR |
| Volume | 11 | Issue | Pages | 661-664 | |
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| Abstract | (Feb):661−664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier. |
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| Language | Summary Language | Original Title | |||
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| ISSN | 1532-4435 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010c | Serial | 1286 | ||
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