Year: 2008
Type: article
Abstract: We present the intentional kernel as a new class of kernel functions for structured data. The class is highly contrasted to the convolution kernel, that is a typical class of kernel functions. That is, the convolution kernel is defined with sub-structures, while the intentional kernel is based on derivations constracting structures. We show instances of the intentional kernel for boolean functions, first-order terms, context sensitive languages, and RNA sequences. We also show some properties of the intentional kernel, and discuss the difference between the intentional kernel and the convolution kernel.
DOI: 10.1527/tjsai.23.185
Authors: Koichiro Doi, Tetsuya Yamashita, Takayuki Tanaka, Akihiro Yamamoto
Institution: Kyoto University
Language: English
Topic: Machine Learning and ELM
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2029578388