Few shot learning for multi-class classification based on nested ensemble DSVM
Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a nove...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Wei [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Sialorphin and its analog as ligands for copper(II) ions - Kamysz, Elżbieta ELSEVIER, 2013transfer abstract, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:98 ; year:2020 ; day:1 ; month:03 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.adhoc.2019.102055 |
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ELV049031678 |
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| 520 | |a Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. | ||
| 520 | |a Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. | ||
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10.1016/j.adhoc.2019.102055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000870.pica (DE-627)ELV049031678 (ELSEVIER)S1570-8705(19)31000-5 DE-627 ger DE-627 rakwb eng 540 VZ 300 610 VZ 44.06 bkl Wang, Wei verfasserin aut Few shot learning for multi-class classification based on nested ensemble DSVM 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Zhang, Li oth Zhang, Mengjun oth Wang, Zhixiong oth Enthalten in Elsevier Science Kamysz, Elżbieta ELSEVIER Sialorphin and its analog as ligands for copper(II) ions 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV016769562 volume:98 year:2020 day:1 month:03 pages:0 https://doi.org/10.1016/j.adhoc.2019.102055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.06 Medizinsoziologie VZ AR 98 2020 1 0301 0 |
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10.1016/j.adhoc.2019.102055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000870.pica (DE-627)ELV049031678 (ELSEVIER)S1570-8705(19)31000-5 DE-627 ger DE-627 rakwb eng 540 VZ 300 610 VZ 44.06 bkl Wang, Wei verfasserin aut Few shot learning for multi-class classification based on nested ensemble DSVM 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Zhang, Li oth Zhang, Mengjun oth Wang, Zhixiong oth Enthalten in Elsevier Science Kamysz, Elżbieta ELSEVIER Sialorphin and its analog as ligands for copper(II) ions 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV016769562 volume:98 year:2020 day:1 month:03 pages:0 https://doi.org/10.1016/j.adhoc.2019.102055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.06 Medizinsoziologie VZ AR 98 2020 1 0301 0 |
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10.1016/j.adhoc.2019.102055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000870.pica (DE-627)ELV049031678 (ELSEVIER)S1570-8705(19)31000-5 DE-627 ger DE-627 rakwb eng 540 VZ 300 610 VZ 44.06 bkl Wang, Wei verfasserin aut Few shot learning for multi-class classification based on nested ensemble DSVM 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Zhang, Li oth Zhang, Mengjun oth Wang, Zhixiong oth Enthalten in Elsevier Science Kamysz, Elżbieta ELSEVIER Sialorphin and its analog as ligands for copper(II) ions 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV016769562 volume:98 year:2020 day:1 month:03 pages:0 https://doi.org/10.1016/j.adhoc.2019.102055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.06 Medizinsoziologie VZ AR 98 2020 1 0301 0 |
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10.1016/j.adhoc.2019.102055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000870.pica (DE-627)ELV049031678 (ELSEVIER)S1570-8705(19)31000-5 DE-627 ger DE-627 rakwb eng 540 VZ 300 610 VZ 44.06 bkl Wang, Wei verfasserin aut Few shot learning for multi-class classification based on nested ensemble DSVM 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Zhang, Li oth Zhang, Mengjun oth Wang, Zhixiong oth Enthalten in Elsevier Science Kamysz, Elżbieta ELSEVIER Sialorphin and its analog as ligands for copper(II) ions 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV016769562 volume:98 year:2020 day:1 month:03 pages:0 https://doi.org/10.1016/j.adhoc.2019.102055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.06 Medizinsoziologie VZ AR 98 2020 1 0301 0 |
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10.1016/j.adhoc.2019.102055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000870.pica (DE-627)ELV049031678 (ELSEVIER)S1570-8705(19)31000-5 DE-627 ger DE-627 rakwb eng 540 VZ 300 610 VZ 44.06 bkl Wang, Wei verfasserin aut Few shot learning for multi-class classification based on nested ensemble DSVM 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. Zhang, Li oth Zhang, Mengjun oth Wang, Zhixiong oth Enthalten in Elsevier Science Kamysz, Elżbieta ELSEVIER Sialorphin and its analog as ligands for copper(II) ions 2013transfer abstract Amsterdam [u.a.] (DE-627)ELV016769562 volume:98 year:2020 day:1 month:03 pages:0 https://doi.org/10.1016/j.adhoc.2019.102055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.06 Medizinsoziologie VZ AR 98 2020 1 0301 0 |
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Sialorphin and its analog as ligands for copper(II) ions |
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| author |
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Few shot learning for multi-class classification based on nested ensemble DSVM |
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Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. |
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Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. |
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Few shot learning (FSL) is a challenge task because a large amount of labeled samples are difficult to acquire due to privacy, ethic issues, safety concerns. Nevertheless, present approaches for multi-class classification usually require massive labeled samples. To bridge this gap, we propose a novel approach named the Nested Ensemble Deep Support Vector Machine (NE-DSVM). In this method, we firstly assign three different single-kernel functions to the deep support vector (DSVM) and ensemble a base classifier in the inner layer. Then, in the outer layer, we use the “one-to-rest” (OvR) strategy to convert the multi-class classification into a binary-class classification. In the last, we combine the AdaBoost framework to complete the classification task. To evaluate the effectiveness of the proposed method, we build a through wall human being detection system and conduct five-fold cross validation experiments on three datasets. We further compare it with SVM, DSVM, AdaBoost-SVM algorithm on the same training and testing data. The experimental results show that the proposed NE-DSVM algorithm lead to encouraging results for few shot learning. |
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Few shot learning for multi-class classification based on nested ensemble DSVM |
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Zhang, Li Zhang, Mengjun Wang, Zhixiong |
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