It is known theoretically that an algorithm cannot be good for an arbitrary prior. We show that in practical terms this also applies to the technique of ``cross validation'', which has been widely regarded as defying this general rule. Numerical examples are analysed in detail. Their implications to researches on learning algorithms are discussed.
|Number of pages||6|
|Publication status||Published - 1 Oct 1996|
Bibliographical noteCopyright of the Massachusetts Institute of Technology Press (MIT Press)
- learning algorithms
- cross validation