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Estimating relatedness via data compression (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Juba, Brendan |
| Description | We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2006-01-01 |
| Publisher Institution | In Proceedings of the 23 rd International Conference on Machine Learning |
| Access Restriction | Open |
| Subject Keyword | Unknown Computable Task Distribution Uniform Bound Independent Sample Empirical Average Error Various Task True Average Error Finite Set Data Compression |
| Content Type | Text |
| Resource Type | Article |
