Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel methods for relation extraction
The Journal of Machine Learning Research
Kernels and Distances for Structured Data
Machine Learning
Classifying protein fingerprints
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Kernels over relational algebra structures
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
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In this paper we propose a new class of kernels defined over extended relational algebra structures. The “extension” was recently proposed in [1] and it overcomes one of the main limitation of the standard relational algebra, i.e. difficulties in modeling lists. These new kernels belong to the class of $\mathcal{R}$-Convolution kernels in the sense that the computation of the similarity between two complex objects is based on the similarities of objects' parts computed by means of subkernels. The complex objects (relational instances in our case) are tuples and sets and/or lists of relational instances for which elementary kernels and kernels on sets and lists are applied. The performance of this class of kernels together with the Support Vector Machines (SVM) algorithm is evaluated on the problem of classification of protein fingerprints and by combining different data representations we were able to improve the best accuracy reported so far in the literature.