| تعداد نشریات | 24 |
| تعداد شمارهها | 851 |
| تعداد مقالات | 7,575 |
| تعداد مشاهده مقاله | 13,413,722 |
| تعداد دریافت فایل اصل مقاله | 11,580,441 |
A GENERALIZED KERNEL-BASED RANDOM K-SAMPLESETS METHOD FOR TRANSFER LEARNING | ||
| Iranian Journal of Science and Technology Transactions of Electrical Engineering | ||
| شناسنامه علمی شماره، دوره 39، E2، اسفند 2015، صفحه 193-207 اصل مقاله (1.03 M) | ||
| شناسه دیجیتال (DOI): 10.22099/ijste.2015.3491 | ||
| نویسندگان | ||
| J. TAHMORESNEZHAD؛ S. HASHEMI* | ||
| Electrical and Computer Engineering School, Shiraz University, Shiraz, I. R. of Iran | ||
| چکیده | ||
| Transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. Feature selection for transfer learning (f-MMD) is a simple and effective transfer learning method, which tackles the domain shift problem. f-MMD has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive performance of f-MMD is challenged by the application domains with large number of examples and features, and ii) f-MMD considers the domain shift problem in fully unsupervised manner. In this paper, we propose a new approach to break down the large initial set of samples into a number of small-sized random subsets, called samplesets. Moreover, we present a feature weighting and instance clustering approach, which categorizes the original feature samplesets into the variant and invariant features. In domain shift problem, invariant features have a vital role in transferring knowledge across domains. The proposed method is called RAkET (RAndom k samplesETs), where k is a parameter that determines the size of the samplesets. Empirical evidence indicates that RAkET manages to improve substantially over f-MMD, especially in domains with large number of features and examples. We evaluate RAkET against other well-known transfer learning methods on synthetic and real world datasets. | ||
| کلیدواژهها | ||
| Transfer learning؛ unsupervised domain adaptation؛ random samplesets؛ feature weighting؛ instance clustering | ||
|
آمار تعداد مشاهده مقاله: 2,707 تعداد دریافت فایل اصل مقاله: 3,010 |
||