PAC learning using Nadaraya-Watson estimator based on orthonormal systems
Conference
·
OSTI ID:527900
- Fort Valley State College, GA (United States). Dept. of Mathematics and Physics
- Oak Ridge National Lab., TN (United States)
Regression or function classes of Euclidean type with compact support and certain smoothness properties are shown to be PAC learnable by the Nadaraya-Watson estimator based on complete orthonormal systems. While requiring more smoothness properties than typical PAC formulations, this estimator is computationally efficient, easy to implement, and known to perform well in a number of practical applications. The sample sizes necessary for PAC learning of regressions or functions under sup norm cost are derived for a general orthonormal system. The result covers the widely used estimators based on Haar wavelets, trignometric functions, and Daubechies wavelets.
- Research Organization:
- Oak Ridge National Lab., TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 527900
- Report Number(s):
- CONF-971067--1; ON: DE97007785
- Country of Publication:
- United States
- Language:
- English
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