Statistical learning theory considers three main problems, pattern recognition, regression and density estimation. This paper studies solvability of these problems (mainly concentrating on pattern recognition and density estimation) in the “high-dimensional” case, where the patterns in the training and test sets are never repeated. We show that, assuming an i.i.d. data source but without any further assumptions, the problems of pattern recognition and regression can often be solved (and there are practically useful algorithms to solve them). On the other hand, the problem of density estimation, as we formalize it, cannot be solved under the general i.i.d. assumption, and additional assumptions are required.
CITATION STYLE
Nouretdinov, I., Vovk, V., Vyugin, M., & Gammerman, A. (2001). Pattern recognition and density estimation under the general i.i.d. assumption. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2111, pp. 337–353). Springer Verlag. https://doi.org/10.1007/3-540-44581-1_22
Mendeley helps you to discover research relevant for your work.