Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Learning of Boolean Functions Using Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
On a Capacity Control Using Boolean Kernels for the Learning of Boolean Functions
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernels and Distances for Structured Data
Machine Learning
Support vector inductive logic programming
DS'05 Proceedings of the 8th international conference on Discovery Science
Kernel Functions Based on Derivation
New Frontiers in Applied Data Mining
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In this paper, we give an algorithm for computing the value of the kernel function KTERM, which takes a pair of terms in first-order logic as its inputs, and facilitates Support Vector Machines classifying terms in a higher dimension space. The value of KTERM(s,t) is given as the total number of terms which subsume both sand t. The algorithm presented in the paper computes KTERM(s,t) without enumerating all such terms. We also implement the algorithm and present some experimental examples of classification of first-order terms with KTERM. Furthermore, we also propose the concept of intentional kernels as a generalization of KTERM.