Name
Abstract | ||
|---|---|---|
This paper presents a kernel function class KRNA which is based on the concept of the intentional kernel (Doi et al., 2006) as opposed to that of the convolution kernel (Haussler, 1999). A kernel function in KRNA computes the similarity between two RNA sequences from the viewpoint of secondary structures. As an instance of KRNA, we give the definition and the algorithm of KRNAN which takes a pair of RNA sequences as its inputs, and facilitates Support Vector Machine (SVM) classifying RNA sequences in a higher dimension space. Our experimental results show a high performance of KRNAN, compared with the string kernel which is a convolution kernel. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-75488-6_30 | Discovery Science |
Keywords | Field | DocType |
rna classification,classifying rna sequence,intentional kernel,rna sequence,convolution kernel,kernel function,vector machine,kernel function class krna,intentional kernel function,string kernel,facilitates support,support vector machine,secondary structure | Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Computer science,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel (image processing),String kernel,Variable kernel density estimation,Machine learning,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
4755 | 0302-9743 | 3-540-75487-3 |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroshi Sankoh | 1 | 16 | 6.68 |
| Koichiro Doi | 2 | 31 | 7.59 |
| Akihiro Yamamoto | 3 | 135 | 26.84 |