Fuzzy deep belief networks for semi-supervised sentiment classification
By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhou, Shusen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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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:131 ; year:2014 ; day:5 ; month:05 ; pages:312-322 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2013.10.011 |
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ELV017631572 |
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| 520 | |a By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. | ||
| 520 | |a By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. | ||
| 650 | 7 | |a Deep learning |2 Elsevier | |
| 650 | 7 | |a Fuzzy sets |2 Elsevier | |
| 650 | 7 | |a Supervised learning |2 Elsevier | |
| 650 | 7 | |a Sentiment classification |2 Elsevier | |
| 700 | 1 | |a Chen, Qingcai |4 oth | |
| 700 | 1 | |a Wang, Xiaolong |4 oth | |
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10.1016/j.neucom.2013.10.011 doi GBVA2014014000023.pica (DE-627)ELV017631572 (ELSEVIER)S0925-2312(13)00969-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Zhou, Shusen verfasserin aut Fuzzy deep belief networks for semi-supervised sentiment classification 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. Deep learning Elsevier Fuzzy sets Elsevier Supervised learning Elsevier Sentiment classification Elsevier Chen, Qingcai oth Wang, Xiaolong 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:131 year:2014 day:5 month:05 pages:312-322 extent:11 https://doi.org/10.1016/j.neucom.2013.10.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 131 2014 5 0505 312-322 11 045F 610 |
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10.1016/j.neucom.2013.10.011 doi GBVA2014014000023.pica (DE-627)ELV017631572 (ELSEVIER)S0925-2312(13)00969-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Zhou, Shusen verfasserin aut Fuzzy deep belief networks for semi-supervised sentiment classification 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. Deep learning Elsevier Fuzzy sets Elsevier Supervised learning Elsevier Sentiment classification Elsevier Chen, Qingcai oth Wang, Xiaolong 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:131 year:2014 day:5 month:05 pages:312-322 extent:11 https://doi.org/10.1016/j.neucom.2013.10.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 131 2014 5 0505 312-322 11 045F 610 |
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10.1016/j.neucom.2013.10.011 doi GBVA2014014000023.pica (DE-627)ELV017631572 (ELSEVIER)S0925-2312(13)00969-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Zhou, Shusen verfasserin aut Fuzzy deep belief networks for semi-supervised sentiment classification 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. Deep learning Elsevier Fuzzy sets Elsevier Supervised learning Elsevier Sentiment classification Elsevier Chen, Qingcai oth Wang, Xiaolong 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:131 year:2014 day:5 month:05 pages:312-322 extent:11 https://doi.org/10.1016/j.neucom.2013.10.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 131 2014 5 0505 312-322 11 045F 610 |
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10.1016/j.neucom.2013.10.011 doi GBVA2014014000023.pica (DE-627)ELV017631572 (ELSEVIER)S0925-2312(13)00969-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Zhou, Shusen verfasserin aut Fuzzy deep belief networks for semi-supervised sentiment classification 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. Deep learning Elsevier Fuzzy sets Elsevier Supervised learning Elsevier Sentiment classification Elsevier Chen, Qingcai oth Wang, Xiaolong 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:131 year:2014 day:5 month:05 pages:312-322 extent:11 https://doi.org/10.1016/j.neucom.2013.10.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 131 2014 5 0505 312-322 11 045F 610 |
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10.1016/j.neucom.2013.10.011 doi GBVA2014014000023.pica (DE-627)ELV017631572 (ELSEVIER)S0925-2312(13)00969-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Zhou, Shusen verfasserin aut Fuzzy deep belief networks for semi-supervised sentiment classification 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. Deep learning Elsevier Fuzzy sets Elsevier Supervised learning Elsevier Sentiment classification Elsevier Chen, Qingcai oth Wang, Xiaolong 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:131 year:2014 day:5 month:05 pages:312-322 extent:11 https://doi.org/10.1016/j.neucom.2013.10.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 131 2014 5 0505 312-322 11 045F 610 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:131 year:2014 day:5 month:05 pages:312-322 extent:11 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. |
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By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. |
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By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods. |
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