Upper-bound estimates for classifiers based on a dissimilarity function

Authors:
B. P. Rusyn;V. A. Tayanov;O. A. Lutsyka
Affiliations:
G. V. Karpenko Physico-Mechanical Institute, National Academy of Sciences of Ukraine, Lviv, Ukraine;G. V. Karpenko Physico-Mechanical Institute, National Academy of Sciences of Ukraine, Lviv, Ukraine;G. V. Karpenko Physico-Mechanical Institute, National Academy of Sciences of Ukraine, Lviv, Ukraine
Venue:
Cybernetics and Systems Analysis
Year:
2012

Citing 2
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Approaches to calculating upper-bound estimates of recognition probability are proposed that can be used for a more general class of models. One of estimates determines the stability of object coverage by classification algorithms on the basis of distribution of distances between objects, and another estimate is underlain by leave-one-out cross-validation. This considerably simplifies and facilitates the construction of estimates.