Publications
2018
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Generative Kernels for Tree-Structured Data Journal Article
In: Neural Networks and Learning Systems, IEEE Transactions on, 2018, ISSN: 2162-2388 .
Abstract | Links | BibTeX | Tags: hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data
@article{tnnlsTreeKer17,
title = {Generative Kernels for Tree-Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/TNNLS.2017.2785292},
issn = {2162-2388 },
year = {2018},
date = {2018-01-15},
journal = {Neural Networks and Learning Systems, IEEE Transactions on},
abstract = {The paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Further, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. The paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping.},
keywords = {hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data},
pubstate = {published},
tppubtype = {article}
}
The paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Further, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. The paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping.2016
Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli
A reservoir activation kernel for trees Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16), i6doc.com, 2016, ISBN: 978-287587027-.
Links | BibTeX | Tags: Echo state networks, kernel methods, reservoir computing, structured data processing, tree kernel, tree structured data
@conference{esann2016,
title = {A reservoir activation kernel for trees},
author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio
},
editor = {M. Verleysen},
url = {https://www.researchgate.net/profile/Claudio_Gallicchio/publication/313236954_A_Reservoir_Activation_Kernel_for_Trees/links/58a9db0892851cf0e3c6b8df/A-Reservoir-Activation-Kernel-for-Trees.pdf},
isbn = {978-287587027-},
year = {2016},
date = {2016-04-29},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16)},
pages = {29-34},
publisher = { i6doc.com},
keywords = {Echo state networks, kernel methods, reservoir computing, structured data processing, tree kernel, tree structured data},
pubstate = {published},
tppubtype = {conference}
}
2014
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Integrating bi-directional contexts in a generative kernel for trees Conference
Neural Networks (IJCNN), 2014 International Joint Conference on, IEEE, 2014.
Links | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data, tree transductions
@conference{11568_586070,
title = {Integrating bi-directional contexts in a generative kernel for trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/IJCNN.2014.6889768},
year = {2014},
date = {2014-01-01},
booktitle = {Neural Networks (IJCNN), 2014 International Joint Conference on},
pages = {4145--4151},
publisher = {IEEE},
keywords = {generative model, graphical models, hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data, tree transductions},
pubstate = {published},
tppubtype = {conference}
}
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Modeling Bi-directional Tree Contexts by Generative Transductions Conference
Neural Information Processing, vol. 8834, Springer International Publishing, 2014.
Abstract | Links | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, kernel methods, tree kernel, tree structured data
@conference{11568_665864,
title = {Modeling Bi-directional Tree Contexts by Generative Transductions},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://dx.doi.org/10.1007/978-3-319-12637-1_68},
doi = {10.1007/978-3-319-12637-1_68},
year = {2014},
date = {2014-01-01},
booktitle = {Neural Information Processing},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {8834},
pages = {543--550},
publisher = {Springer International Publishing},
abstract = {We introduce an approach to integrate bi-directional contexts in a generative tree model by means of structured transductions. We show how this can be efficiently realized as the composition of a top-down and a bottom-up generative model for trees, that are trained independently within a circular encoding-decoding scheme. The resulting input-driven generative model is shown to capture information concerning bi-directional contexts within its state-space. An experimental evaluation using the Jaccard generative kernel for trees is presented, indicating that the approach can achieve state of the art performance on tree classification benchmarks.},
keywords = {generative model, graphical models, hidden tree Markov model, kernel methods, tree kernel, tree structured data},
pubstate = {published},
tppubtype = {conference}
}
We introduce an approach to integrate bi-directional contexts in a generative tree model by means of structured transductions. We show how this can be efficiently realized as the composition of a top-down and a bottom-up generative model for trees, that are trained independently within a circular encoding-decoding scheme. The resulting input-driven generative model is shown to capture information concerning bi-directional contexts within its state-space. An experimental evaluation using the Jaccard generative kernel for trees is presented, indicating that the approach can achieve state of the art performance on tree classification benchmarks.2012
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
A Generative Multiset Kernel for Structured Data Conference
Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series, vol. 7552, Springer-Verlag, BERLIN HEIDELBERG, 2012.
Abstract | Links | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, kernel methods, structured data processing, support vector machine, tree kernel, tree structured data
@conference{11568_156516,
title = {A Generative Multiset Kernel for Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1007/978-3-642-33269-2_8},
year = {2012},
date = {2012-01-01},
booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {7552},
pages = {57--64},
publisher = {Springer-Verlag},
address = {BERLIN HEIDELBERG},
abstract = {The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.},
keywords = {generative model, graphical models, hidden tree Markov model, kernel methods, structured data processing, support vector machine, tree kernel, tree structured data},
pubstate = {published},
tppubtype = {conference}
}
The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.2011
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Adaptive Tree Kernel by Multinomial Generative Topographic Mapping Conference
Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway (NJ), 2011.
Links | BibTeX | Tags: generative model, generative topographic mapping, graphical models, hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data
@conference{11568_145907,
title = {Adaptive Tree Kernel by Multinomial Generative Topographic Mapping},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6033423&contentType=Conference+Publications&refinements%3D4294413850%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6033131%29},
doi = {10.1109/IJCNN.2011.6033423},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the International Joint Conference on Neural Networks},
pages = {1651--1658},
publisher = {IEEE},
address = {Piscataway (NJ)},
keywords = {generative model, generative topographic mapping, graphical models, hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data},
pubstate = {published},
tppubtype = {conference}
}