Learn More
Haussler's convolution kernel provides a successful framework for engineering new positive semidefinite kernels, and has been applied to a wide range of data types and applications. In the framework, each data object represents a finite set of finer grained components. Then, Haussler's convolution kernel takes a pair of data objects as input, and returns(More)
We propose a novel general-purpose tree kernel and apply it to glycan structure analysis. Our kernel measures the similarity between two labeled trees by counting the number of common q-length substrings (tree q-grams) embedded in the trees for all possible lengths q. We apply our tree kernel using a support vector machine (SVM) to classification and(More)
Functions counting the number of common sub-patterns between trees have been promising candidates for kernel functions for trees in learning systems. There are several viewpoints of how two patterns between two trees can be regarded as the same. In the tree edit distance , these viewpoints have been well formalized as the class of tree mappings, and several(More)
This thesis presents a unified understanding of edit-based approaches to approximate tree matching and introduces new facts on the subject. It also provides a broad view of kernel-based learning methods for trees, and proposes novel methods based on this view. These contributions have a wide range of applications to pattern matching, computational biology,(More)
The notion of metabolic closure is presented and analyzed in terms of Robert Rosen's theory of (M, R) systems. Recent results concerning (M, R) systems are reviewed, specially those defining self-referential equations like f (f) = f. We relate (M, R) systems to Autopoiesis, another theory centered on metabolic closure, and we speculate how an algebraic view(More)