Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34487-9_48 | ICONIP (3) |
Keywords | Field | DocType |
recursive similarity-based learning algorithm,recursive similarity-based algorithm,inputs information,illustrative example,gaussian kernel,uci machine learning repository,new feature,feature space,new features similarity,deep learning idea,distance-based method,machine learning,k nearest neighbors | Active learning (machine learning),Computer science,Wake-sleep algorithm,Artificial intelligence,Deep learning,Recursion,k-nearest neighbors algorithm,Online machine learning,Feature vector,Stability (learning theory),Pattern recognition,Algorithm,Machine learning | Conference |
Volume | ISSN | Citations |
7665 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| tomasz maszczyk | 1 | 42 | 5.29 |
| Włodzisław Duch | 2 | 291 | 28.95 |