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Volumn 1, Issue , 2011, Pages 126-130

Empirical learning aided by weak domain knowledge in the form of feature importance

Author keywords

Domain knowledge; Feature importance; Hybrid learning

Indexed keywords

DOMAIN KNOWLEDGE; FEATURE IMPORTANCE; HYBRID LEARNING; NEURAL NETWORK ALGORITHM; PRIOR KNOWLEDGE; RELATIVE IMPORTANCE; SIMPLE MODIFICATIONS;

EID: 80051890017     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CMSP.2011.32     Document Type: Conference Paper
Times cited : (15)

References (17)
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  • 8
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    • Distributional word clusters vs. Words for text categorization
    • Bekkerman, R., El-Yaniv, R., Tishby, N., and Winter., Y. Distributional word clusters vs. words for text categorization. JMLR, 3 (2003), 1183-1208.
    • (2003) JMLR , vol.3 , pp. 1183-1208
    • Bekkerman, R.1    El-Yaniv, R.2    Tishby, N.3    Winter, Y.4
  • 10
    • 80051881303 scopus 로고    scopus 로고
    • The feature importance ranking measure
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    • McGraw-Hill Science/Engineering/Math
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    • Greedy function approximation: A gradient boosting machine
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    • Training knowledge-based neural networks to recognize genes in DNA sequences
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.