Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2006
Abstract
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel Machines (UKM) from both labeled and unlabeled data. Our proposed framework integrates supervised learning, semi-supervised kernel learning, and active learning in a unified solution. In the suggested framework, we particularly focus our attention on designing a new semi-supervised kernel learning method, i.e., Spectral Kernel Learning (SKL), which is built on the principles of kernel target alignment and unsupervised kernel design. Our algorithm is related to an equivalent quadratic programming problem that can be efficiently solved. Empirical results have shown that our method is more effective and robust to learn the semi-supervised kernels than traditional approaches. Based on the framework, we present a specific paradigm of unified kernel machines with respect to Kernel Logistic Regresions (KLR), i.e., Unified Kernel Logistic Regression (UKLR). We evaluate our proposed UKLR classification scheme in comparison with traditional solutions. The promising results show that our proposed UKLR paradigm is more effective than the traditional classification approaches.
Keywords
Classification, Kernel Machines, Spectral Kernel Learning, Supervised Learning, Semi-Supervised Learning, Unsupervised Kernel Design, Kernel Logistic Regressions, Active Learning
Discipline
Computer Sciences | Databases and Information Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
KDD '06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, August 20-23
First Page
187
Last Page
196
ISBN
9781595933393
Identifier
10.1145/1150402.1150426
Publisher
ACM
City or Country
New York
Citation
HOI, Steven C. H.; LYU, Michael R.; and CHANG, Edward Y..
Learning the unified Kernel machines for classification. (2006). KDD '06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, August 20-23. 187-196.
Available at: https://ink.library.smu.edu.sg/sis_research/2388
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/1150402.1150426