Intentional Kernel Functions
Koichiro Doi, Tetsuya Yamashita, Takayuki Tanaka, Akihiro Yamamoto · Transactions of the Japanese Society for Artificial Intelligence · 2008
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.