Online multiple kernel classification
Although both online learning and kernel learning have been studied extensively in machine learning, there is limited effort in addressing the intersecting research problems of these two important topics. As an attempt to fill the gap, we address a new research problem, termed Online Multiple Kernel...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/84500 http://hdl.handle.net/10220/17285 |
| _version_ | 1811681777087938560 |
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| author | Jin, Rong. Zhao, Peilin. Yang, Tianbao. Hoi, Steven C. H. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Jin, Rong. Zhao, Peilin. Yang, Tianbao. Hoi, Steven C. H. |
| author_sort | Jin, Rong. |
| collection | NTU |
| description | Although both online learning and kernel learning have been studied extensively in machine learning, there is limited effort in addressing the intersecting research problems of these two important topics. As an attempt to fill the gap, we address a new research problem, termed Online Multiple Kernel Classification (OMKC), which learns a kernel-based prediction function by selecting a subset of predefined kernel functions in an online learning fashion. OMKC is in general more challenging than typical online learning because both the kernel classifiers and the subset of selected kernels are unknown, and more importantly the solutions to the kernel classifiers and their combination weights are correlated. The proposed algorithms are based on the fusion of two online learning algorithms, i.e., the Perceptron algorithm that learns a classifier for a given kernel, and the Hedge algorithm that combines classifiers by linear weights. We develop stochastic selection strategies that randomly select a subset of kernels for combination and model updating, thus improving the learning efficiency. Our empirical study with 15 data sets shows promising performance of the proposed algorithms for OMKC in both learning efficiency and prediction accuracy. |
| first_indexed | 2024-10-01T03:46:20Z |
| format | Journal Article |
| id | ntu-10356/84500 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:46:20Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/845002020-05-28T07:18:24Z Online multiple kernel classification Jin, Rong. Zhao, Peilin. Yang, Tianbao. Hoi, Steven C. H. School of Computer Engineering DRNTU::Engineering::Computer science and engineering Although both online learning and kernel learning have been studied extensively in machine learning, there is limited effort in addressing the intersecting research problems of these two important topics. As an attempt to fill the gap, we address a new research problem, termed Online Multiple Kernel Classification (OMKC), which learns a kernel-based prediction function by selecting a subset of predefined kernel functions in an online learning fashion. OMKC is in general more challenging than typical online learning because both the kernel classifiers and the subset of selected kernels are unknown, and more importantly the solutions to the kernel classifiers and their combination weights are correlated. The proposed algorithms are based on the fusion of two online learning algorithms, i.e., the Perceptron algorithm that learns a classifier for a given kernel, and the Hedge algorithm that combines classifiers by linear weights. We develop stochastic selection strategies that randomly select a subset of kernels for combination and model updating, thus improving the learning efficiency. Our empirical study with 15 data sets shows promising performance of the proposed algorithms for OMKC in both learning efficiency and prediction accuracy. 2013-11-05T06:14:21Z 2019-12-06T15:46:10Z 2013-11-05T06:14:21Z 2019-12-06T15:46:10Z 2012 2012 Journal Article Hoi, S. C. H., Jin, R., Zhao, P., & Yang, T. (2013). Online Multiple Kernel Classification. Machine Learning, 90(2), 289-316. https://hdl.handle.net/10356/84500 http://hdl.handle.net/10220/17285 10.1007/s10994-012-5319-2 en Machine learning |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Jin, Rong. Zhao, Peilin. Yang, Tianbao. Hoi, Steven C. H. Online multiple kernel classification |
| title | Online multiple kernel classification |
| title_full | Online multiple kernel classification |
| title_fullStr | Online multiple kernel classification |
| title_full_unstemmed | Online multiple kernel classification |
| title_short | Online multiple kernel classification |
| title_sort | online multiple kernel classification |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/84500 http://hdl.handle.net/10220/17285 |
| work_keys_str_mv | AT jinrong onlinemultiplekernelclassification AT zhaopeilin onlinemultiplekernelclassification AT yangtianbao onlinemultiplekernelclassification AT hoistevench onlinemultiplekernelclassification |