Abstrakt:
Recursive Similarity-Based Learning algorithm (RSBL) follows the
deep learning idea, exploiting similarity-based methodology to recursively generate
new features. Each transformation layer is generated separately, using as
inputs information from all previous layers, and as new features similarity to the
k nearest neighbors scaled using Gaussian kernels. In the feature space created in
this way results of various types of classifiers, including linear discrimination and
distance-based methods, are significantly improved. As an illustrative example a
few non-trivial benchmark datasets from the UCI Machine Learning Repository
are analyzed.
