Ordering as privileged information
| Autor: | Vacek, Thomas |
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| Rok vydání: | 2016 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | We propose to accelerate the rate of convergence of the pattern recognition task by directly minimizing the variance diameters of certain hypothesis spaces, which are critical quantities in fast-convergence results.We show that the variance diameters can be controlled by dividing hypothesis spaces into metric balls based on a new order metric. This order metric can be minimized as an ordinal regression problem, leading to a LUPI (Learning Using Privileged Information) application where we take the privileged information as some desired ordering, and construct a faster-converging hypothesis space by empirically restricting some larger hypothesis space according to that ordering. We give a risk analysis of the approach. We discuss the difficulties with model selection and give an innovative technique for selecting multiple model parameters. Finally, we provide some data experiments. Comment: 10 pages, 1 table, 2 page appendix giving proofs |
| Databáze: | arXiv |
| Externí odkaz: |
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