تقرير
A Hybrid Instance-based Transfer Learning Method
| العنوان: | A Hybrid Instance-based Transfer Learning Method |
|---|---|
| المؤلفون: | Asgarian, Azin, Sobhani, Parinaz, Zhang, Ji Chao, Mihailescu, Madalin, Sibilia, Ariel, Ashraf, Ahmed Bilal, Taati, Babak |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning |
| الوصف: | In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult to collect sufficiently large training datasets. Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target). In this work, we propose a hybrid instance-based transfer learning method that outperforms a set of baselines including state-of-the-art instance-based transfer learning approaches. Our method uses a probabilistic weighting strategy to fuse information from the source domain to the model learned in the target domain. Our method is generic, applicable to multiple source domains, and robust with respect to negative transfer. We demonstrate the effectiveness of our approach through extensive experiments for two different applications. Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1812.01063 |
| رقم الأكسشن: | edsarx.1812.01063 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |