메뉴 건너뛰기




Volumn 35, Issue 4, 2011, Pages 369-382

BSPNN: Boosted subspace probabilistic neural network for email security

Author keywords

[No Author keywords available]

Indexed keywords

ADVANCED INFORMATION SYSTEMS; CONVENTIONAL DETECTION; CYBER THREATS; DETECTION ACCURACY; E-MAIL SECURITY; EMPIRICAL ANALYSIS; ENSEMBLE LEARNING; HUMAN ABILITIES; INTERNET CONNECTIVITY; LARGE DATABASE; MACHINE LEARNING ALGORITHMS; MACHINE LEARNING TECHNOLOGY; NUMBER OF FALSE ALARMS; PERFORMANCE ISSUES; POTENTIAL APPLICATIONS; PREDICTIVE MODELS; PROBABILISTIC NEURAL NETWORKS; RADIAL BASIS FUNCTION NEURAL NETWORKS; SECURITY BREACHES; SECURITY DETECTION; SECURITY INFORMATICS;

EID: 79955946761     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-010-9198-2     Document Type: Article
Times cited : (28)

References (24)
  • 2
    • 22544468455 scopus 로고    scopus 로고
    • Boosting trees for anti-spam email filtering
    • Tzigov Chark, Bulgaria
    • Carreras X, MarquezL (2001) Boosting trees for anti-spam email filtering. In: RANLP, Tzigov Chark, Bulgaria
    • (2001) RANLP
    • Carreras, X.1    Marquez, L.2
  • 4
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constricting ensembles of decision trees: Bagging, boosting and randomization
    • Dietterich TG (2000) An experimental comparison of three methods for constricting ensembles of decision trees: bagging, boosting and randomization. Mach Learn 40:139-158
    • (2000) Mach Learn , vol.40 , pp. 139-158
    • Dietterich, T.G.1
  • 5
    • 0031211090 scopus 로고    scopus 로고
    • A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
    • Freund Y, Schapire R (1997) A decision-theoretic generation of on-line learning and an application to boosting. J Comput Syst Sci 55:119-139 (Pubitemid 127433398)
    • (1997) Journal of Computer and System Sciences , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 6
    • 0034164230 scopus 로고    scopus 로고
    • ADDITIVE LOGISTIC REGRESSION: A STATISTICAL VIEW OF BOOSTING
    • Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337-407 (Pubitemid 33227445)
    • (2000) Annals of Statistics , vol.28 , Issue.SPI2 , pp. 337-407
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 9
    • 70449106947 scopus 로고    scopus 로고
    • Efficient multiclass boosting classification with active learning
    • Huang J, Ertekin S, Song Y, Zha H, Giles CL (2007) Efficient multiclass boosting classification with active learning. In: ICDM
    • (2007) ICDM
    • Huang, J.1    Ertekin, S.2    Song, Y.3    Zha, H.4    Giles, C.L.5
  • 17
    • 0000040021 scopus 로고    scopus 로고
    • Using output codes to boost multiclass learning problems
    • Schapire R (1997) Using output codes to boost multiclass learning problems. In: Proceedings of ICML, pp 313-321
    • (1997) Proceedings of ICML , pp. 313-321
    • Schapire, R.1
  • 18
    • 0026254768 scopus 로고
    • A general regression neural network
    • Spetch DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568-576
    • (1991) IEEE Trans Neural Netw , vol.2 , pp. 568-576
    • Spetch, D.F.1
  • 21
    • 0032124116 scopus 로고    scopus 로고
    • Introduction to the modified probabilistic neural network for general signal processing applications
    • PII S1053587X98044134
    • Zaknich A (1998) Introduction to the modified probabilistic neural network for general signal processing applications. IEEE Trans Signal Process 46:1980-1990 (Pubitemid 128743991)
    • (1998) IEEE Transactions on Signal Processing , vol.46 , Issue.7 , pp. 1980-1990
    • Zaknich, A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.