메뉴 건너뛰기




Volumn 21, Issue 1, 2009, Pages 272-300

Prototype classification: Insights from machine learning

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; FACTUAL DATABASE; HUMAN; PERCEPTIVE DISCRIMINATION; STATISTICAL MODEL;

EID: 59149094573     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2009.01-07-443     Document Type: Article
Times cited : (31)

References (23)
  • 2
    • 0000275022 scopus 로고    scopus 로고
    • Prediction games and arcing algorithms
    • Breiman, L. (1999). Prediction games and arcing algorithms. Neural Computation, 11(7), 1493-1518.
    • (1999) Neural Computation , vol.11 , Issue.7 , pp. 1493-1518
    • Breiman, L.1
  • 6
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G., & Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.1    Salakhutdinov, R.2
  • 8
    • 0031375503 scopus 로고    scopus 로고
    • The perceptron algorithm vs. winnow: Linear vs. logarithmic mistake bounds when few input variables are relevant
    • Kivinen, J., Warmuth, M., & Auer, P. (1997). The perceptron algorithm vs. winnow: Linear vs. logarithmic mistake bounds when few input variables are relevant. Artificial Intelligence, 97(1-2), 325-343.
    • (1997) Artificial Intelligence , vol.97 , Issue.1-2 , pp. 325-343
    • Kivinen, J.1    Warmuth, M.2    Auer, P.3
  • 9
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • Lee, D., & Seung, H. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788-791.
    • (1999) Nature , vol.401 , pp. 788-791
    • Lee, D.1    Seung, H.2
  • 11
    • 1542276975 scopus 로고    scopus 로고
    • An introduction to boosting and leveraging
    • New York: Springer
    • Rätsch, G., & Meir, G. (2003). An introduction to boosting and leveraging. In Advanced lectures on machine learning (Vol. LNAI 2600, pp. 119-184). New York: Springer.
    • (2003) In Advanced lectures on machine learning , vol.LNAI 2600 , pp. 119-184
    • Rätsch, G.1    Meir, G.2
  • 13
    • 0001147815 scopus 로고
    • Pattern recognition and categorization
    • Reed, S. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382-407.
    • (1972) Cognitive Psychology , vol.3 , pp. 382-407
    • Reed, S.1
  • 15
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis, S., & Saul, L. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-2326.
    • (2000) Science , vol.290 , pp. 2323-2326
    • Roweis, S.1    Saul, L.2
  • 16
  • 17
    • 0035370188 scopus 로고    scopus 로고
    • Drifting games
    • Schapire, R. (2001). Drifting games. Machine Learning, 43(3), 265-291.
    • (2001) Machine Learning , vol.43 , Issue.3 , pp. 265-291
    • Schapire, R.1
  • 18
    • 0031211090 scopus 로고    scopus 로고
    • A decision theoretic generalization of on-line learning and an application to boosting
    • Schapire, R., & Freund, Y. (1997). A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Schapire, R.1    Freund, Y.2
  • 19
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • Schapire, R., Freund, Y., Bartlett, P., & Lee, W. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5), 1651-1686.
    • (1998) Annals of Statistics , vol.26 , Issue.5 , pp. 1651-1686
    • Schapire, R.1    Freund, Y.2    Bartlett, P.3    Lee, W.4
  • 21
    • 0036080160 scopus 로고    scopus 로고
    • Bagging, boosting and the random subspace method for linear classifiers
    • Skurichina, M., & Duin, R. (2002). Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis and Applications, 5, 121-135.
    • (2002) Pattern Analysis and Applications , vol.5 , pp. 121-135
    • Skurichina, M.1    Duin, R.2
  • 22
    • 0001224048 scopus 로고    scopus 로고
    • Sparse Bayesian learning and the relevance vector machine
    • Tipping, M. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1, 211-214.
    • (2001) Journal of Machine Learning Research , vol.1 , pp. 211-214
    • Tipping, M.1
  • 23
    • 59149091427 scopus 로고    scopus 로고
    • Vapnik, V. (2000). The nature of statistical learning theory (2nd ed.). New York: Springer. Wickens, T. (2002). Elementary signal detection theory. New York: Oxford University Press.
    • Vapnik, V. (2000). The nature of statistical learning theory (2nd ed.). New York: Springer. Wickens, T. (2002). Elementary signal detection theory. New York: Oxford University Press.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.