In this paper we explain the fundamental idea of designing a class of kernel functions, called the intentional kernel, for structured data. The intentional kernel is designed with the property that every structured data is defined by derivation. Derivation means transforming a data or an expression into another. Typical derivation can be found in the field of formal language theory: A grammar defines a language in the sense that a sequence belongs to a language if it is transformed from a starting symbol by repeated application of the production rules in the grammar. Another example is in mathematical logic: A formula is proved if it is obtained from axioms by repeated application of inference rules. Combining derivation with the kernel-based learning mechanism derives the class of the intentional kernel. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Doi, K., & Yamamoto, A. (2009). Kernel functions based on derivation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5433 LNAI, pp. 111–122). Springer Verlag. https://doi.org/10.1007/978-3-642-00399-8_10
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