Instance Transfer Learning with Multisource Dynamic TrAdaBoost
Since the transfer learning can employ knowledge in relative domains to help the learning tasks in current target domain, compared with the traditional learning it shows the advantages of reducing the learning cost and improving the learning efficiency. Focused on the situation that sample data from...
Saved in:
Main Authors: | Qian Zhang, Haigang Li, Yong Zhang, Ming Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2014-01-01
|
Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2014/282747 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A droplet state prediction method for inkjet printing under small sample conditions based on the two-stage TrAdaBoost.R2 algorithm
by: Yuanyuan Jia, et al.
Published: (2025-01-01) -
Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations
by: Younghyun Lee, et al.
Published: (2013-01-01) -
Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree
by: Yibai Li, et al.
Published: (2022-01-01) -
Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier
by: Jigang Li, et al.
Published: (2019-01-01) -
Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost
by: Hongzhi Zhou, et al.
Published: (2021-01-01)