Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/603635
Title: Generalization Performance of Regularized Ranking With Multiscale Kernels
Authors: Yicong Zhou;Hong Chen;Rushi Lan;Zhibin Pan
subject: generalization performance|ranking|multiscale kernel|Drug discovery|recommendation tasks|reproducing kernel Hilbert space (RKHS).
Year: 2016
Publisher: IEEE
Abstract: The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.
URI: http://localhost/handle/Hannan/135979
http://localhost/handle/Hannan/603635
ISSN: 2162-237X
2162-2388
volume: 27
issue: 5
Appears in Collections:2016

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Title: Generalization Performance of Regularized Ranking With Multiscale Kernels
Authors: Yicong Zhou;Hong Chen;Rushi Lan;Zhibin Pan
subject: generalization performance|ranking|multiscale kernel|Drug discovery|recommendation tasks|reproducing kernel Hilbert space (RKHS).
Year: 2016
Publisher: IEEE
Abstract: The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.
URI: http://localhost/handle/Hannan/135979
http://localhost/handle/Hannan/603635
ISSN: 2162-237X
2162-2388
volume: 27
issue: 5
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7118736.pdf990.97 kBAdobe PDFThumbnail
Preview File
Title: Generalization Performance of Regularized Ranking With Multiscale Kernels
Authors: Yicong Zhou;Hong Chen;Rushi Lan;Zhibin Pan
subject: generalization performance|ranking|multiscale kernel|Drug discovery|recommendation tasks|reproducing kernel Hilbert space (RKHS).
Year: 2016
Publisher: IEEE
Abstract: The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.
URI: http://localhost/handle/Hannan/135979
http://localhost/handle/Hannan/603635
ISSN: 2162-237X
2162-2388
volume: 27
issue: 5
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7118736.pdf990.97 kBAdobe PDFThumbnail
Preview File