Regression Error Characteristic Curves

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 43-50

  Bi, Jinbo ^a   Bennett, Kristin P ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR ANALYSIS; FUNCTIONS; LAPLACE TRANSFORMS; MATHEMATICAL MODELS; METRIC SYSTEM; PROBABILITY; SPURIOUS SIGNAL NOISE; WEIBULL DISTRIBUTION;

BINARY CLASSIFICATION MODELS; ERROR TOLERANCE; RECEIVER OPERATING CHARACTERISTICS (ROC); REGRESSION ERROR CHARACTERISTICS (REC);

REGRESSION ANALYSIS;

EID: 1942421195     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. DeGroot, M. H. (1986). Probability and statistics. MA: Addison-Wesley.
2. Egan, J. P. (1975). Signal detection theory and ROC analysis. In Series in cognition and perception. New York: Scientific Press.
3. Fawcett, T. (2003). ROC graphs: Notes and practical considerations for data mining researchers (Technical Report HPL-2003-4). Hewlett Packard, Palo Alto, CA.
4. Provost, F., Fawcett, T., & Kohavi, R. (1998). The case against accuracy estimation for comparing induction algorithms. Proceedings of the Fifteenth International Conference on Machine Learning (pp. 445-453). San Francisco: Morgan Kaufmann.
5. Rosipal, R., & Trejo, L. (2001). Kernel partial least squares regression in reproducing kernel hilbert space. Journal of Machine Learning Research, 2, 97-123.
6. Swets, J. A., Dawes, R. M., & Monahan, J. (2000). Better decisions through science. Scientific America, 283, 82-87.
7. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.
8. Wold, H. (1966). Estimation of principle components and related models by iterative least squares. Multivariate Analysis (pp. 391-420). New York: Academic Press.