Name
Abstract | ||
|---|---|---|
We define a novel kernel function for finite sequences of arbitrary length which we call the path kernel. We evaluate this kernel in a classification scenario using synthetic data sequences and show that our kernel can outperform state of the art sequential similarity measures. Furthermore, we find that, in our experiments, a clustering of data based on the path kernel results in much improved interpretability of such clusters compared to alternative approaches such as dynamic time warping or the global alignment kernel. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-12610-4_5 | PATTERN RECOGNITION APPLICATIONS AND METHODS, ICPRAM 2013 |
Keywords | Field | DocType |
Kernels,Sequences | Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Computer science,Tree kernel,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,String kernel,Variable kernel density estimation,Machine learning,Kernel (statistics) | Conference |
Volume | ISSN | Citations |
318 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Baisero | 1 | 0 | 0.34 |
| Florian T. Pokorny | 2 | 158 | 20.07 |
| Danica Kragic | 3 | 2070 | 142.17 |
| carl henrik ek | 4 | 327 | 30.76 |