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Related Experiment Videos

Domain transfer multiple kernel learning.

Lixin Duan¹, Ivor W Tsang, Dong Xu

¹Nanyang Technological University, Nanyang Avenue, Singapore 639798. S080003@ntu.edu.sg

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 8, 2011

Summary

This summary is machine-generated.

Domain Transfer Multiple Kernel Learning (DTMKL) improves cross-domain classification by learning kernels and classifiers simultaneously. This approach effectively adapts models to new domains with limited data, outperforming existing methods.

Related Experiment Videos

Area of Science:

Machine Learning
Artificial Intelligence
Computer Vision

Background:

Cross-domain learning leverages auxiliary domain data for target domain classification with limited samples.
Significant feature distribution shifts between domains pose a challenge for traditional methods.

Purpose of the Study:

To propose a novel cross-domain kernel learning framework, Domain Transfer Multiple Kernel Learning (DTMKL).
To integrate existing kernel methods and address distribution mismatch between domains.

Main Methods:

DTMKL framework simultaneously learns a kernel function and a classifier.
Minimizes structural risk and distribution mismatch between auxiliary and target domains.
Introduces two novel methods using SVM and pre-learned classifiers within the DTMKL framework.

Main Results:

DTMKL-based methods demonstrated superior performance on TRECVID, 20 Newsgroups, and email spam datasets.
Outperformed existing cross-domain learning and multiple kernel learning techniques.
Effectively handles considerable changes in feature distributions across domains.

Conclusions:

DTMKL offers a robust and adaptable framework for cross-domain learning.
The proposed methods show significant improvements in domain adaptation tasks.
DTMKL provides a unified approach for incorporating various kernel methods in cross-domain scenarios.