Sparse nonparametric topic model for transfer learning
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Kustantaja | i6doc.com publication |
| Sivut | 269-274 |
| Sivumäärä | 6 |
| ISBN (painettu) | 9782874190490 |
| Tila | Julkaistu - 2012 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 - Bruges, Belgia Kesto: 25 huhtikuuta 2012 → 27 huhtikuuta 2012 |
Conference
| Conference | 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 |
|---|---|
| Maa | Belgia |
| Kaupunki | Bruges |
| Ajanjakso | 25/04/12 → 27/04/12 |
Tiivistelmä
Count data arises for example in bioinformatics or analysis of text documents represented as word count vectors. With several data sets available from related sources, exploiting their similarities by transfer learning can improve models compared to modeling sources independently. We introduce a Bayesian generative transfer learning model which represents similarity across document collections by sparse sharing of latent topics controlled by an Indian Buffet Process. Unlike Hierarchical Dirichlet Process based multi-task learning, our model decouples topic sharing probability from topic strength, making sharing of low-strength topics easier, and outperforms the HDP approach in experiments.