Name
Abstract | ||
|---|---|---|
In many applications data is naturally presented in terms of orderings of some basic elements or symbols. Reasoning about such data requires a notion of similarity capable of handling sequences of dierent lengths. In this paper we describe a family of Mercer kernel functions for such sequentially structured data. The family is characterized by a decomposable structure in terms of symbol-level and structure-level similarities, representing a specic combination of kernels which allows for ecient computation. We provide an experimental evaluation on sequential classication tasks comparing kernels from our family of kernels to a state of the art sequence kernel called the Global Alignment kernel which has been shown to outperform Dynamic Time Warping. |
Year | Venue | Field |
|---|---|---|
2015 | arXiv: Learning | Kernel (linear algebra),Dynamic time warping,Pairwise sequence alignment,Algorithm,Data model,Mathematics,Computation,Kernel (statistics) |
DocType | Volume | Citations |
Journal | abs/1501.06284 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Baisero | 1 | 3 | 1.40 |
| Florian T. Pokorny | 2 | 158 | 20.07 |
| carl henrik ek | 3 | 327 | 30.76 |