Name
Abstract | ||
|---|---|---|
All existing learning methods have particular bias that makes them suitable for specific kind of problems. Universal Learning Machine (ULM) should find the simplest data model for arbitrary data distributions. Several ways to create ULMs are outlined, and an algorithm based on creation of new global and local features combined with meta-learning is introduced. This algorithm is able to find simple solutions that sophisticated algorithms ignore, learn complex Boolean functions, complicated probability distributions, as well as the problems requiring multiresolution decision borders. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10684-2_23 | ICONIP |
Keywords | Field | DocType |
universal learning machines,sophisticated algorithm,arbitrary data distribution,complex boolean function,universal learning machine,simplest data model,existing learning method,multiresolution decision border,particular bias,local feature,complicated probability distribution,data model,boolean function,probability distribution | Boolean function,Learning machine,Projection pursuit,Computer science,Theoretical computer science,Probability distribution,Artificial intelligence,Adaptive regularization,Data model,Machine learning | Conference |
Volume | ISSN | Citations |
5864 | 0302-9743 | 5 |
PageRank | References | Authors |
0.49 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Włodzisław Duch | 1 | 291 | 28.95 |
| tomasz maszczyk | 2 | 42 | 5.29 |