Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Using attributed plex grammars for the generation of image and graph databases
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Object recognition using multiresolution trees
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
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Several real-world problems (e.g., in bioinformatics/proteomics, or in recognition of video sequences) can be described as classification tasks over sequences of structured data, i.e. sequences of graphs, in a natural way. This paper presents a novel machine that can learn and carry out decision-making over sequences of graphical data. The machine involves a hidden Markov model whose state-emission probabilities are defined over graphs. This is realized by combining recursive encoding networks and constrained radial basis function networks. A global optimization algorithm which regards to the machine as a unity (instead of a bare superposition of separate modules) is introduced, via gradient-ascent over the maximum-likelihood criterion within a Baum-Welch-like forward-backward procedure. To the best of our knowledge, this is the first machine learning approach capable of processing sequences of graphs without the need of a pre-processing step. Preliminary results are reported.