[1] Breiman L. (1996), Bagging predictors; Machine Learning 26; 123-140.
[2] Breiman L. (1999), Random forests-Random Features; Technical Report 567; Statistics Department,
University of California, Berkeley, http://ftp.stat.berkeley.edu/pub/users/breimanf,4 July 2007.
[3] Bureau of labour statistics (1996), Characteristics of injuries and illnesses resulting in absences from
work, 1994 Washington, DC: US Department of Labour; Bureau of Labour Statistics, USDL; 96–163.
[4] Dempsey P.G., Ayoub M.M., Westfall P.H. (1995), The NIOSH lifting equations: a closer look. In:
Bitner A.C. and Champney P.C. (Ed); Advance in Industrial Ergonomics and Safety VII. Taylor and
Francis, Bristol, PA; 705–712.
[5] Dempsey P.G., Westfall P.H. (1997), Developing explicit risk models for predicting low-back
disability: a statistical perspective; International Journal of Industrial Ergonomics 19; 483–497.
[6] Driessens K., Reutemann P., Pfahringer B., Leschi C.(2006), Using Weighted Nearest Neighbour to
Benefit from Unlabeled Data.In:Wee Keong Ng,Masaru Kitsuregawa, Jianzhong Li, and Kuiyu Chang,
(Ed); Advances in Knowledge Discovery and Data Mining, 10th Pacific-AsiaConference, PAKDD
2006, 3918 of LNCS; 60-69.
[7] Feller W. (1968), An Introduction to Probability Theory and Its Application; third edition, Vol. 1,
Wiley; New York.
[8] Frymoyer J.W., Cats-Baril W.L. (1991), An overview of the incidence and costs of low-back pain;
Orthopaedic Clinics of North America 22; 262–271.
[9] Guo H., Tanaka S., Cameron L., Seligman P., Behrens V., Ger J. (1995), Back pain among workers in
the United States: national estimates and workers at high risks; American Journal of Industrial
Medicine 28; 591–602.
[10] Karwowski W., Zurada J., Marras W.S., Gaddie P. (1994), A prototype of the artificial neural networkbased
system for classification of industrial jobs with respect to risk of low back disorders. In:
Aghazadeh F. (Ed); Advances in Industrial Ergonomics and Safety VI, Taylor & Francis, Bristol, PA;
19–22.
[11] Killough M.K., Crumpton L.L., Calvert A., Bowden R. (1995), An investigation using neural networks
to identify the presence of carpal tunnel syndrome; 4th Industrial Engineering Research Conference
Proceedings, IIE, Norcross, GA; 659–667.
[12] Marras W.S., Lavender S.A., Leurgans S., Sudhakar L.R., Allread W.G., Fathallah F., Ferguson S.
(1993), The role of dynamic three dimensional trunk motion in occupationally-related low back
disorders; Spine 18; 617–628.
[13] Pal M., Mather P.M. (2003), An assessment of the effectiveness of decision tree methods for land
cover classification; Remote Sensing of Environment 86; 554–565.
[14] Quinlan J.R. (1993), C4.5: Programs for Machine Learning; Morgan Kaufmann, San Mateo.
[15] Seeger M. (2002), Learning with labeled and unlabeled data; Technical report, Institute for Adaptive
and Neural Computation, University of Edinburgh,
http://www.kyb.tuebingen.mpg.de/bs/people/seeger/papers/review.pdf, 13 May 2007.
[16] Tanaka S., Wild D., Seligman P., Halperin W., Behrens V., Putz-Anderson V. (1995), Prevalence and
work-relatedness of self-reported carpal tunnel syndrome among US workers: Analyses of the
occupational health supplement data to the 1988 National Health Interview Survey; American Journal
of Industrial Medicine 27; 451–470.
[17] Webster B.S., Snook S.H. (1994), The cost of compensable upper extremity cumulative trauma
disorders; Journal of Occupational and Environmental Medicine 36; 713–717.
[18] Zhu X. (2006), Semi-supervised learning literature survey; Technical Report 1530, Department of
Computer Sciences, University of Wisconsin-Madison,
http://www.pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf, 8 May 2007.
[19] Zurada J., Karwowski W., Marras W.S. (1997), A neural network-based system for classification of
industrial jobs with respect to risk of low back disorders due to work-place design; Applied
Ergonomics 28; 49–58.