Support Vector Machine incorporated with feature discrimination
Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we f...
Ausführliche Beschreibung
Autor*in: |
Wang, Yunyun [verfasserIn] Chen, Songcan [verfasserIn] Xue, Hui [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2011 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 38, Seite 12506-12513 |
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Übergeordnetes Werk: |
volume:38 ; pages:12506-12513 |
DOI / URN: |
10.1016/j.eswa.2011.04.034 |
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Katalog-ID: |
ELV008298475 |
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520 | |a Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. | ||
650 | 4 | |a Weight vector | |
650 | 4 | |a Feature (attribute) discrimination | |
650 | 4 | |a Weight penalization matrix | |
650 | 4 | |a Support Vector Machine | |
650 | 4 | |a Pattern classification | |
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700 | 1 | |a Xue, Hui |e verfasserin |4 aut | |
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10.1016/j.eswa.2011.04.034 doi (DE-627)ELV008298475 (ELSEVIER)S0957-4174(11)00550-1 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Wang, Yunyun verfasserin aut Support Vector Machine incorporated with feature discrimination 2011 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. Weight vector Feature (attribute) discrimination Weight penalization matrix Support Vector Machine Pattern classification Chen, Songcan verfasserin aut Xue, Hui verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 38, Seite 12506-12513 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:38 pages:12506-12513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 38 12506-12513 |
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10.1016/j.eswa.2011.04.034 doi (DE-627)ELV008298475 (ELSEVIER)S0957-4174(11)00550-1 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Wang, Yunyun verfasserin aut Support Vector Machine incorporated with feature discrimination 2011 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. Weight vector Feature (attribute) discrimination Weight penalization matrix Support Vector Machine Pattern classification Chen, Songcan verfasserin aut Xue, Hui verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 38, Seite 12506-12513 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:38 pages:12506-12513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 38 12506-12513 |
allfields_unstemmed |
10.1016/j.eswa.2011.04.034 doi (DE-627)ELV008298475 (ELSEVIER)S0957-4174(11)00550-1 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Wang, Yunyun verfasserin aut Support Vector Machine incorporated with feature discrimination 2011 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. Weight vector Feature (attribute) discrimination Weight penalization matrix Support Vector Machine Pattern classification Chen, Songcan verfasserin aut Xue, Hui verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 38, Seite 12506-12513 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:38 pages:12506-12513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 38 12506-12513 |
allfieldsGer |
10.1016/j.eswa.2011.04.034 doi (DE-627)ELV008298475 (ELSEVIER)S0957-4174(11)00550-1 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Wang, Yunyun verfasserin aut Support Vector Machine incorporated with feature discrimination 2011 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. Weight vector Feature (attribute) discrimination Weight penalization matrix Support Vector Machine Pattern classification Chen, Songcan verfasserin aut Xue, Hui verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 38, Seite 12506-12513 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:38 pages:12506-12513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 38 12506-12513 |
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10.1016/j.eswa.2011.04.034 doi (DE-627)ELV008298475 (ELSEVIER)S0957-4174(11)00550-1 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Wang, Yunyun verfasserin aut Support Vector Machine incorporated with feature discrimination 2011 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. Weight vector Feature (attribute) discrimination Weight penalization matrix Support Vector Machine Pattern classification Chen, Songcan verfasserin aut Xue, Hui verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 38, Seite 12506-12513 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:38 pages:12506-12513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 38 12506-12513 |
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Wang, Yunyun @@aut@@ Chen, Songcan @@aut@@ Xue, Hui @@aut@@ |
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Wang, Yunyun |
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Wang, Yunyun ddc 004 bkl 54.72 misc Weight vector misc Feature (attribute) discrimination misc Weight penalization matrix misc Support Vector Machine misc Pattern classification Support Vector Machine incorporated with feature discrimination |
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support vector machine incorporated with feature discrimination |
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Support Vector Machine incorporated with feature discrimination |
abstract |
Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. |
abstractGer |
Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. |
abstract_unstemmed |
Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency. |
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Support Vector Machine incorporated with feature discrimination |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV008298475</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524153724.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230508s2011 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.eswa.2011.04.034</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV008298475</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0957-4174(11)00550-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Yunyun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Support Vector Machine incorporated with feature discrimination</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Support Vector Machine (SVM) achieves state-of-the-art performance in many real applications. A guarantee of its performance superiority is from the maximization of between-class margin, or loosely speaking, full use of discriminative information from between-class samples. While in this paper, we focus on not only such discriminative information from samples but also discrimination of individual features and develop feature discrimination incorporated SVM (FDSVM). Instead of minimizing the l2-norm of feature weight vector, or equivalently, imposing equal penalization on all weight components in SVM learning, FDSVM penalizes each weight by an amount decreasing with the corresponding feature discrimination measure, consequently features with better discrimination can be attached greater importance. Experiments on both toy and real UCI datasets demonstrate that FDSVM often achieves better performance with comparable efficiency.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weight vector</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature (attribute) discrimination</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weight penalization matrix</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Support Vector Machine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pattern classification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Songcan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xue, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Expert systems with applications</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1990</subfield><subfield code="g">38, Seite 12506-12513</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320577961</subfield><subfield code="w">(DE-600)2017237-0</subfield><subfield code="w">(DE-576)11481807X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:38</subfield><subfield code="g">pages:12506-12513</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield 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