Back to Search
Start Over
Designing a Testing Framework for Transfer Learning Algorithms (Application Paper)
- Source :
- IRI
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for transfer learning algorithms. With this in mind, a test framework is proposed, comprising of distortion profiles which define a comprehensive test suite. The unique contribution of this paper is the definition of a test framework that measures a more complete profile of a transfer learning algorithm's capability, facilitating the identification of relative poor and good performance areas. As a proof of concept, the test framework is used to test a homogeneous transfer learning algorithm. The test framework will be the basis for a number of future applications.
- Subjects :
- Weighted Majority Algorithm
Wake-sleep algorithm
business.industry
Computer science
Stability (learning theory)
02 engineering and technology
010501 environmental sciences
Machine learning
computer.software_genre
01 natural sciences
Test (assessment)
Proof of concept
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Test suite
Test Management Approach
Artificial intelligence
Data mining
Transfer of learning
business
Algorithm
computer
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)
- Accession number :
- edsair.doi...........4a9ecc5eba511f0a0784d044f5e92cc8