Learning with Recurrent Neural Networks
Learning with Recurrent Neural Networks
Self-Organizing Maps
Relational Data Mining
Application of Cascade Correlation Networks for Structures toChemistry
Applied Intelligence
Logo Recognition by Recursive Neural Networks
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Similarity learning for graph-based image representations
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Hidden Tree Markov Models for Document Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards Incremental Parsing of Natural Language Using Recursive Neural Networks
Applied Intelligence
Architectural bias in recurrent neural networks: fractal analysis
Neural Computation
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Glycan classification with tree kernels
Bioinformatics
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The graph neural network model
IEEE Transactions on Neural Networks
Neural network for graphs: a contextual constructive approach
IEEE Transactions on Neural Networks
A new tree kernel based on SOM-SD
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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
Architectural and Markovian factors of echo state networks
Neural Networks
Neural networks for relational learning: an experimental comparison
Machine Learning
A subpath kernel for rooted unordered trees
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Statistical relational learning: an inductive logic programming perspective
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Classifying relational data with neural networks
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
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
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Markovian architectural bias of recurrent neural networks
IEEE Transactions on Neural Networks
Contextual processing of structured data by recursive cascade correlation
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
An input-output hidden Markov model for tree transductions
Neurocomputing
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In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained generalized recursive reservoir, exhibiting extreme efficiency for learning in structured domains. In addition, we highlight through the paper other characteristics of the approach: First, we discuss the Markovian characterization of reservoir dynamics, extended to the case of tree domains, that is implied by the contractive setting of the TreeESN state transition function. Second, we study two types of state mapping functions to map the tree structured state of TreeESN into a fixed-size feature representation for classification or regression tasks. The critical role of the relation between the choice of the state mapping function and the Markovian characterization of the task is analyzed and experimentally investigated on both artificial and real-world tasks. Finally, experimental results on benchmark and real-world tasks show that the TreeESN approach, in spite of its efficiency, can achieve comparable results with state-of-the-art, although more complex, neural and kernel based models for tree structured data.