Hidden Tree Markov Models for Document Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
A survey on tree edit distance and related problems
Theoretical Computer Science
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Exact algorithms for computing the tree edit distance between unordered trees
Theoretical Computer Science
Bottom-up generative modeling of tree-structured data
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Computational methods for hidden Markov tree models-an application to wavelet trees
IEEE Transactions on Signal Processing
Learning dynamic audio-visual mapping with input-output Hidden Markov models
IEEE Transactions on Multimedia
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Experiments on the application of IOHMMs to model financial returns series
IEEE Transactions on Neural Networks
Visualization of Tree-Structured Data Through Generative Topographic Mapping
IEEE Transactions on Neural Networks
Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data
IEEE Transactions on Neural Networks
Neurocomputing
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The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model to non-homogeneous state transition and emission probabilities. We show how the proposed input-driven approach can be used to realize different types of structured transductions between trees. A thorough experimental analysis is proposed to investigate the advantage of introducing an input-driven dynamics in structured-data processing. The results of this analysis suggest that input-driven models can capture more discriminative structural information than homogeneous approaches in computational learning tasks, including document classification and more general substructure categorization.