Name
Abstract | ||
|---|---|---|
We present a deep prototype-based learning architecture which achieves a performance that is competitive to a conventional, shallow prototype-based model but at a fraction of the computational cost, especially w.r.t. memory requirements. As prototype-based classification and regression methods are typically plagued by the exploding number of prototypes necessary to solve complex problems, this is an important step towards efficient prototype-based classification and regression. We demonstrate these claims by benchmarking our deep prototype-based model on the well-known MNIST dataset. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-44781-0_15 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT II |
Keywords | Field | DocType |
Prototype-based learning, Pattern recognition, Deep learning, Incremental learning | MNIST database,Computer science,Learning architecture,Incremental learning,Artificial intelligence,Deep learning,Machine learning,Benchmarking,Complex problems | Conference |
Volume | ISSN | Citations |
9887 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Hecht | 1 | 2 | 0.37 |
| Alexander Gepperth | 2 | 2 | 0.37 |