Multi-label classification using a cascade of stacked autoencoder and extreme learning machines
• Three phase cascade of neural networks for multi-label classification. • Network model includes stacked autoencoders and extreme learning machines. • Stacked autoencoder reduces complex input features to appropriate representation. • Multi-label extreme learning machine used for soft classificatio...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Law, Anwesha [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Umfang: |
13 |
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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 |
|---|---|
| Übergeordnetes Werk: |
volume:358 ; year:2019 ; day:17 ; month:09 ; pages:222-234 ; extent:13 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2019.05.051 |
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| 520 | |a • Three phase cascade of neural networks for multi-label classification. • Network model includes stacked autoencoders and extreme learning machines. • Stacked autoencoder reduces complex input features to appropriate representation. • Multi-label extreme learning machine used for soft classification. • Soft classification scores mapped to hard labels with an extreme learning machine. | ||
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multi-label classification using a cascade of stacked autoencoder and extreme learning machines |
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Multi-label classification using a cascade of stacked autoencoder and extreme learning machines |
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• Three phase cascade of neural networks for multi-label classification. • Network model includes stacked autoencoders and extreme learning machines. • Stacked autoencoder reduces complex input features to appropriate representation. • Multi-label extreme learning machine used for soft classification. • Soft classification scores mapped to hard labels with an extreme learning machine. |
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• Three phase cascade of neural networks for multi-label classification. • Network model includes stacked autoencoders and extreme learning machines. • Stacked autoencoder reduces complex input features to appropriate representation. • Multi-label extreme learning machine used for soft classification. • Soft classification scores mapped to hard labels with an extreme learning machine. |
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• Three phase cascade of neural networks for multi-label classification. • Network model includes stacked autoencoders and extreme learning machines. • Stacked autoencoder reduces complex input features to appropriate representation. • Multi-label extreme learning machine used for soft classification. • Soft classification scores mapped to hard labels with an extreme learning machine. |
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Multi-label classification using a cascade of stacked autoencoder and extreme learning machines |
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code="g">pages:222-234</subfield><subfield code="g">extent:13</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2019.05.051</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">358</subfield><subfield code="j">2019</subfield><subfield code="b">17</subfield><subfield code="c">0917</subfield><subfield code="h">222-234</subfield><subfield code="g">13</subfield></datafield></record></collection>
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