A pre-selecting base kernel method in multiple kernel learning
The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Peng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:165 ; year:2015 ; day:1 ; month:10 ; pages:46-53 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.neucom.2014.06.094 |
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ELV013183532 |
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| 245 | 1 | 0 | |a A pre-selecting base kernel method in multiple kernel learning |
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| 520 | |a The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. | ||
| 520 | |a The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. | ||
| 650 | 7 | |a Minimal redundancy maximal relevance |2 Elsevier | |
| 650 | 7 | |a Multiple kernel learning |2 Elsevier | |
| 650 | 7 | |a Kernel target alignment |2 Elsevier | |
| 650 | 7 | |a Kernel selection |2 Elsevier | |
| 700 | 1 | |a Duan, Fuqing |4 oth | |
| 700 | 1 | |a Guo, Ping |4 oth | |
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2015 |
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10.1016/j.neucom.2014.06.094 doi GBVA2015014000024.pica (DE-627)ELV013183532 (ELSEVIER)S0925-2312(15)00430-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Wu, Peng verfasserin aut A pre-selecting base kernel method in multiple kernel learning 2015transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. Minimal redundancy maximal relevance Elsevier Multiple kernel learning Elsevier Kernel target alignment Elsevier Kernel selection Elsevier Duan, Fuqing oth Guo, Ping oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:165 year:2015 day:1 month:10 pages:46-53 extent:8 https://doi.org/10.1016/j.neucom.2014.06.094 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 165 2015 1 1001 46-53 8 045F 610 |
| spelling |
10.1016/j.neucom.2014.06.094 doi GBVA2015014000024.pica (DE-627)ELV013183532 (ELSEVIER)S0925-2312(15)00430-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Wu, Peng verfasserin aut A pre-selecting base kernel method in multiple kernel learning 2015transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. Minimal redundancy maximal relevance Elsevier Multiple kernel learning Elsevier Kernel target alignment Elsevier Kernel selection Elsevier Duan, Fuqing oth Guo, Ping oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:165 year:2015 day:1 month:10 pages:46-53 extent:8 https://doi.org/10.1016/j.neucom.2014.06.094 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 165 2015 1 1001 46-53 8 045F 610 |
| allfields_unstemmed |
10.1016/j.neucom.2014.06.094 doi GBVA2015014000024.pica (DE-627)ELV013183532 (ELSEVIER)S0925-2312(15)00430-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Wu, Peng verfasserin aut A pre-selecting base kernel method in multiple kernel learning 2015transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. Minimal redundancy maximal relevance Elsevier Multiple kernel learning Elsevier Kernel target alignment Elsevier Kernel selection Elsevier Duan, Fuqing oth Guo, Ping oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:165 year:2015 day:1 month:10 pages:46-53 extent:8 https://doi.org/10.1016/j.neucom.2014.06.094 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 165 2015 1 1001 46-53 8 045F 610 |
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10.1016/j.neucom.2014.06.094 doi GBVA2015014000024.pica (DE-627)ELV013183532 (ELSEVIER)S0925-2312(15)00430-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Wu, Peng verfasserin aut A pre-selecting base kernel method in multiple kernel learning 2015transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. Minimal redundancy maximal relevance Elsevier Multiple kernel learning Elsevier Kernel target alignment Elsevier Kernel selection Elsevier Duan, Fuqing oth Guo, Ping oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:165 year:2015 day:1 month:10 pages:46-53 extent:8 https://doi.org/10.1016/j.neucom.2014.06.094 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 165 2015 1 1001 46-53 8 045F 610 |
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10.1016/j.neucom.2014.06.094 doi GBVA2015014000024.pica (DE-627)ELV013183532 (ELSEVIER)S0925-2312(15)00430-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Wu, Peng verfasserin aut A pre-selecting base kernel method in multiple kernel learning 2015transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. Minimal redundancy maximal relevance Elsevier Multiple kernel learning Elsevier Kernel target alignment Elsevier Kernel selection Elsevier Duan, Fuqing oth Guo, Ping oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:165 year:2015 day:1 month:10 pages:46-53 extent:8 https://doi.org/10.1016/j.neucom.2014.06.094 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 165 2015 1 1001 46-53 8 045F 610 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:165 year:2015 day:1 month:10 pages:46-53 extent:8 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:165 year:2015 day:1 month:10 pages:46-53 extent:8 |
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Minimal redundancy maximal relevance Multiple kernel learning Kernel target alignment Kernel selection |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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Wu, Peng @@aut@@ Duan, Fuqing @@oth@@ Guo, Ping @@oth@@ |
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The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. |
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The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. |
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The pre-defined base kernel greatly affects the performance of multiple kernel learning (MKL), but selecting the pre-defined base kernel still has no theoretical guidance. In practice, it is very difficult to select a set of appropriate base kernels without prior knowledge. In this paper, we propose a general strategy to pre-select a reasonable set of base kernels before the optimization process of MKL solvers. This strategy is based on the combination of minimal redundancy maximal relevance criteria and kernel target alignment (MRMRKA). First, we determine some candidate kernels while maintaining diversity of information; second, a set of base kernels with high discriminative ability and large diversity are selected using the MRMRKA method. These pre-selected base kernels will be used in the optimization process of the existing MKL solvers to generate better results. The experiments conducted on UCI and 15-scene datasets show that the performance of MKL is improved with the proposed pre-selected base kernel strategy. |
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