Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
We introduce a new convolution kernel for labeled ordered trees with arbitrary subgraph features, and an efficient algorithm for computing the kernel with the same time complexity as that of the parse tree kernel. The proposed kernel is extended to allow mutations of labels and structures without increasing the order of computation time. Moreover, as a limit of generalization of the tree kernels, we show a hardness result in computing kernels for unordered rooted labeled trees with arbitrary subgraph features.
Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
Kellen Cheng, Anna Lisa Gentile, et al.
EMNLP 2024
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
Rei Odaira, Jose G. Castanos, et al.
IISWC 2013