Name
Abstract | ||
|---|---|---|
Instance-transfer learning has emerged as a promising learning framework to boost performance of prediction models on newly-arrived tasks. The success of the framework depends on the relevance of the source data to the target data. This paper proposes a new approach to source data selection for instance-transfer learning. The approach is capable of selecting the largest subset S of the source data which relevance to the target data is statistically guaranteed to be the highest among any superset ofS . The approach is formally described and theoretically justied. Experimental results on real-world data sets demonstrate that |
Year | Venue | Field |
|---|---|---|
2015 | ACML | Data mining,Subset and superset,Data set,Pattern recognition,Source data,Computer science,Artificial intelligence,Predictive modelling,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuang Zhou | 1 | 10 | 5.02 |
| Gijs Schoenmakers | 2 | 41 | 7.21 |
| Evgueni N. Smirnov | 3 | 24 | 20.38 |
| Ralf L. M. Peeters | 4 | 62 | 22.61 |
| Kurt Driessens | 5 | 489 | 34.75 |
| Siqi Chen | 6 | 123 | 14.10 |