메뉴 건너뛰기




Volumn 32, Issue , 2008, Pages 355-384

Spectrum of Variable-Random Trees

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; RANDOM PROCESSES;

EID: 52249099075     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2470     Document Type: Article
Times cited : (92)

References (27)
  • 2
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2), 105-139.
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 4
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L. (1996a). Bagging predictors. Machine Learning, 24(2), 123-140.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 5
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • Breiman, L. (1996b). Stacked regressions. Machine Learning, 24(1), 49-64.
    • (1996) Machine Learning , vol.24 , Issue.1 , pp. 49-64
    • Breiman, L.1
  • 6
    • 0034276320 scopus 로고    scopus 로고
    • Randomizing outputs to increase prediction accuracy
    • Breiman, L. (2000). Randomizing outputs to increase prediction accuracy. Machine Learning, 40(3), 229-242.
    • (2000) Machine Learning , vol.40 , Issue.3 , pp. 229-242
    • Breiman, L.1
  • 7
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 9
    • 33646403804 scopus 로고    scopus 로고
    • PERT - perfect random tree ensembles
    • Costa Mesa, Orange Country, California
    • Cutler, A., & Zhao, G. (2001). PERT - perfect random tree ensembles. In Computing Science and Statistics, Vol. 33, pp. 490-497, Costa Mesa, Orange Country, California.
    • (2001) Computing Science and Statistics , vol.33 , pp. 490-497
    • Cutler, A.1    Zhao, G.2
  • 10
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal Machine Learning Research, 7, 1-30.
    • (2006) Journal Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 11
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2), 139-157.
    • (2000) Machine Learning , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.G.1
  • 23
    • 22944453097 scopus 로고    scopus 로고
    • Improving random forests
    • Boulicaut, J.-F, Esposito, E, Giannotti, F, & Pedreschi, D, Eds, Proceedings of The 15th European Conference on Machine Learning ECML 2004, of, Pisa, Italy. Springer
    • Robnik-Šikonja, M. (2004). Improving random forests.. In Boulicaut, J.-F, Esposito, E, Giannotti, F., & Pedreschi, D. (Eds.), Proceedings of The 15th European Conference on Machine Learning (ECML 2004), Vol. 3201 of Lecture Notes in Computer Science, pp. 359-370, Pisa, Italy. Springer.
    • (2004) Lecture Notes in Computer Science , vol.3201 , pp. 359-370
    • Robnik-Šikonja, M.1
  • 25
    • 0034247206 scopus 로고    scopus 로고
    • Multiboosting: A technique for combining boosting and wagging
    • Webb, G. I. (2000). Multiboosting: A technique for combining boosting and wagging. Machince Learning, 40(2), 159-196.
    • (2000) Machince Learning , vol.40 , Issue.2 , pp. 159-196
    • Webb, G.I.1
  • 26
    • 4344706336 scopus 로고    scopus 로고
    • Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques
    • Webb, G. I., & Zheng, Z. (2004). Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques. IEEE Transactions on Knowledge and Data Engineering, 16(8), 980-991.
    • (2004) IEEE Transactions on Knowledge and Data Engineering , vol.16 , Issue.8 , pp. 980-991
    • Webb, G.I.1    Zheng, Z.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.