Learning And Optimization Of The Kernel Functions From Insufficiently Labeled Data
Amongst all the machine learning techniques, kernel methods are increasingly becoming popular due to their efficiency, accuracy and ability to handle high-dimensional data. The fundamental problem related to these learning techniques is the selection of the kernel function. Therefore, learning th...
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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2010
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/41234/1/M._Ehsan_Abbasnejad-shahfiq.pdf |
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| 總結: | Amongst all the machine learning techniques, kernel methods are increasingly becoming
popular due to their efficiency, accuracy and ability to handle high-dimensional
data. The fundamental problem related to these learning techniques is the selection of
the kernel function. Therefore, learning the kernel as a procedure in which the kernel
function is selected for a particular dataset is highly important. In this thesis, two approaches
to learn the kernel function are proposed: transferred learning of the kernel
and an unsupervised approach to learn the kernel. The first approach uses transferred
knowledge from unlabeled data to cope with situations where training examples are
scarce. Unlabeled data is used in conjunction with labeled data to construct an optimized
kernel using Fisher discriminant analysis and maximum mean discrepancy. The
accuracy of classification which indicates the number of correctly predicted test examples
from the base kernels and the optimized kernel are compared in two datasets
involving satellite images and synthetic data where proposed approach produces better
results. The second approach is an unsupervised method to learn a linear combination
of kernel functions. |
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