Accuracy estimation for supervised learning algorithms
Conference
·
OSTI ID:466831
This paper illustrates the relative merits of three methods - k-fold Cross Validation, Error Bounds, and Incremental Halting Test - to estimate the accuracy of a supervised learning algorithm. For each of the three methods we point out the problem they address, some of the important assumptions that are based on, and illustrate them through an example. Finally, we discuss the relative advantages and disadvantages of each method.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 466831
- Report Number(s):
- CONF-970465-7; ON: DE97004709
- Resource Relation:
- Conference: SPIE international conference, Orlando, FL (United States), 21-25 Apr 1997; Other Information: PBD: [1997]
- Country of Publication:
- United States
- Language:
- English
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