Publications
2020
Bacciu, Davide; Errica, Federico; Micheli, Alessio
Probabilistic Learning on Graphs via Contextual Architectures Journal Article
In: Journal of Machine Learning Research, vol. 21, no. 134, pp. 1−39, 2020.
Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing
@article{jmlrCGMM20,
title = {Probabilistic Learning on Graphs via Contextual Architectures},
author = {Davide Bacciu and Federico Errica and Alessio Micheli},
editor = {Pushmeet Kohli},
url = {http://jmlr.org/papers/v21/19-470.html, Paper},
year = {2020},
date = {2020-07-27},
journal = {Journal of Machine Learning Research},
volume = {21},
number = {134},
pages = {1−39},
abstract = {We propose a novel methodology for representation learning on graph-structured data, in which a stack of Bayesian Networks learns different distributions of a vertex's neighborhood. Through an incremental construction policy and layer-wise training, we can build deeper architectures with respect to typical graph convolutional neural networks, with benefits in terms of context spreading between vertices.
First, the model learns from graphs via maximum likelihood estimation without using target labels.
Then, a supervised readout is applied to the learned graph embeddings to deal with graph classification and vertex classification tasks, showing competitive results against neural models for graphs. The computational complexity is linear in the number of edges, facilitating learning on large scale data sets. By studying how depth affects the performances of our model, we discover that a broader context generally improves performances. In turn, this leads to a critical analysis of some benchmarks used in literature.},
keywords = {deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing},
pubstate = {published},
tppubtype = {article}
}
We propose a novel methodology for representation learning on graph-structured data, in which a stack of Bayesian Networks learns different distributions of a vertex's neighborhood. Through an incremental construction policy and layer-wise training, we can build deeper architectures with respect to typical graph convolutional neural networks, with benefits in terms of context spreading between vertices.
First, the model learns from graphs via maximum likelihood estimation without using target labels.
Then, a supervised readout is applied to the learned graph embeddings to deal with graph classification and vertex classification tasks, showing competitive results against neural models for graphs. The computational complexity is linear in the number of edges, facilitating learning on large scale data sets. By studying how depth affects the performances of our model, we discover that a broader context generally improves performances. In turn, this leads to a critical analysis of some benchmarks used in literature.2019
Bacciu, Davide; Sotto, Luigi Di
A non-negative factorization approach to node pooling in graph convolutional neural networks Conference
Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019), Lecture Notes in Artificial Intelligence Springer-Verlag, 2019.
Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing
@conference{aiia2019,
title = {A non-negative factorization approach to node pooling in graph convolutional neural networks},
author = {Davide Bacciu and Luigi {Di Sotto}},
url = {https://arxiv.org/pdf/1909.03287.pdf},
year = {2019},
date = {2019-11-22},
booktitle = {Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019)},
publisher = {Springer-Verlag},
series = {Lecture Notes in Artificial Intelligence},
keywords = {deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing},
pubstate = {published},
tppubtype = {conference}
}
Castellana, Daniele; Bacciu, Davide
Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models Conference
Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019I) , IEEE, 2019.
Abstract | Links | BibTeX | Tags: graphical models, hidden tree Markov model, structured data processing, tree structured data; tensor factorization; Bayesian learning
@conference{ijcnn2019,
title = {Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models},
author = {Daniele Castellana and Davide Bacciu},
url = {https://arxiv.org/pdf/1905.13528.pdf},
year = {2019},
date = {2019-07-15},
booktitle = {Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019I) },
publisher = {IEEE},
abstract = {Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-transition matrix. We propose a new approximation which lies on the Tucker factorisation of tensors. The probabilistic interpretation of such approximation allows us to define a new probabilistic model for tree-structured data. Hence, we define the new approximated model and we derive its learning algorithm. Then, we empirically assess the effective power of the new model evaluating it on two different tasks. In both cases, our model outperforms the other approximated model known in the literature.},
keywords = {graphical models, hidden tree Markov model, structured data processing, tree structured data; tensor factorization; Bayesian learning},
pubstate = {published},
tppubtype = {conference}
}
Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-transition matrix. We propose a new approximation which lies on the Tucker factorisation of tensors. The probabilistic interpretation of such approximation allows us to define a new probabilistic model for tree-structured data. Hence, we define the new approximated model and we derive its learning algorithm. Then, we empirically assess the effective power of the new model evaluating it on two different tasks. In both cases, our model outperforms the other approximated model known in the literature.
Davide, Bacciu; Daniele, Castellana
Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering Journal Article
In: Neurocomputing, vol. 342, pp. 49-59, 2019, ISBN: 0925-2312.
Abstract | Links | BibTeX | Tags: graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning
@article{neucomBayesHTMM,
title = {Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering},
author = {Bacciu Davide and Castellana Daniele},
url = {https://doi.org/10.1016/j.neucom.2018.11.091},
doi = {10.1016/j.neucom.2018.11.091},
isbn = {0925-2312},
year = {2019},
date = {2019-05-21},
journal = {Neurocomputing},
volume = {342},
pages = {49-59},
abstract = {The paper deals with the problem of unsupervised learning with structured data, proposing a mixture model approach to cluster tree samples. First, we discuss how to use the Switching-Parent Hidden Tree Markov Model, a compositional model for learning tree distributions, to define a finite mixture model where the number of components is fixed by a hyperparameter. Then, we show how to relax such an assumption by introducing a Bayesian non-parametric mixture model where the number of necessary hidden tree components is learned from data. Experimental validation on synthetic and real datasets show the benefit of mixture models over simple hidden tree models in clustering applications. Further, we provide a characterization of the behaviour of the two mixture models for different choices of their hyperparameters.},
keywords = {graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning},
pubstate = {published},
tppubtype = {article}
}
The paper deals with the problem of unsupervised learning with structured data, proposing a mixture model approach to cluster tree samples. First, we discuss how to use the Switching-Parent Hidden Tree Markov Model, a compositional model for learning tree distributions, to define a finite mixture model where the number of components is fixed by a hyperparameter. Then, we show how to relax such an assumption by introducing a Bayesian non-parametric mixture model where the number of necessary hidden tree components is learned from data. Experimental validation on synthetic and real datasets show the benefit of mixture models over simple hidden tree models in clustering applications. Further, we provide a characterization of the behaviour of the two mixture models for different choices of their hyperparameters.2018
Davide, Bacciu; Daniele, Castellana
Learning Tree Distributions by Hidden Markov Models Workshop
Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18), 2018.
Links | BibTeX | Tags: graphical models, hidden tree Markov model, structured data processing, tree structured data
@workshop{learnaut18,
title = {Learning Tree Distributions by Hidden Markov Models},
author = {Bacciu Davide and Castellana Daniele},
editor = {Rémi Eyraud and Jeffrey Heinz and Guillaume Rabusseau and Matteo Sammartino },
url = {https://arxiv.org/abs/1805.12372},
year = {2018},
date = {2018-07-13},
booktitle = {Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18)},
keywords = {graphical models, hidden tree Markov model, structured data processing, tree structured data},
pubstate = {published},
tppubtype = {workshop}
}
Davide, Bacciu; Federico, Errica; Alessio, Micheli
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing Conference
Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018.
Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing
@conference{icml2018,
title = {Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing},
author = {Bacciu Davide and Errica Federico and Micheli Alessio},
url = {https://arxiv.org/abs/1805.10636},
year = {2018},
date = {2018-07-11},
booktitle = {Proceedings of the 35th International Conference on Machine Learning (ICML 2018)},
keywords = {deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing},
pubstate = {published},
tppubtype = {conference}
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Davide, Bacciu; Daniele, Castellana
Mixture of Hidden Markov Models as Tree Encoder Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18), i6doc.com, Louvain-la-Neuve, Belgium, 2018, ISBN: 978-287587047-6.
Abstract | BibTeX | Tags: graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning
@conference{esann2018Tree,
title = {Mixture of Hidden Markov Models as Tree Encoder},
author = {Bacciu Davide and Castellana Daniele},
editor = {Michel Verleysen},
isbn = {978-287587047-6},
year = {2018},
date = {2018-04-26},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18)},
pages = {543-548},
publisher = {i6doc.com},
address = {Louvain-la-Neuve, Belgium},
abstract = {The paper introduces a new probabilistic tree encoder based on a mixture of Bottom-up Hidden Tree Markov Models. The ability to recognise similar structures in data is experimentally assessed both in clusterization and classification tasks. The results of these preliminary experiments suggest that the model can be successfully used to compress the tree structural and label patterns in a vectorial representation.},
keywords = {graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
The paper introduces a new probabilistic tree encoder based on a mixture of Bottom-up Hidden Tree Markov Models. The ability to recognise similar structures in data is experimentally assessed both in clusterization and classification tasks. The results of these preliminary experiments suggest that the model can be successfully used to compress the tree structural and label patterns in a vectorial representation.
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.2017
Davide, Bacciu
Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data Conference
Proc. of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI'17), IEEE, 2017.
Links | BibTeX | Tags: deep learning, hidden tree Markov model, structured data processing
@conference{dl2017,
title = {Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data},
author = {Bacciu Davide},
url = {https://arxiv.org/abs/1711.07784},
year = {2017},
date = {2017-11-27},
booktitle = {Proc. of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI'17)},
publisher = {IEEE},
keywords = {deep learning, hidden tree Markov model, structured data processing},
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.2013
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model Journal Article
In: Neural Networks and Learning Systems, IEEE Transactions on, vol. 24, no. 2, pp. 231 -247, 2013, ISSN: 2162-237X.
Links | BibTeX | Tags: generative topographic mapping, hidden Markov models, hidden tree Markov model, self-organizing map, tree structured data
@article{gmtsdII2012,
title = {Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6395856},
doi = {10.1109/TNNLS.2012.2228226},
issn = {2162-237X},
year = {2013},
date = {2013-02-01},
journal = {Neural Networks and Learning Systems, IEEE Transactions on},
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pages = {231 -247},
keywords = {generative topographic mapping, hidden Markov models, hidden tree Markov model, self-organizing map, tree structured data},
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Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
An input–output hidden Markov model for tree transductions Journal Article
In: Neurocomputing, vol. 112, pp. 34–46, 2013, ISSN: 0925-2312.
Links | BibTeX | Tags: hidden Markov models, hidden tree Markov model, structured data processing, tree transductions
@article{bacciuNeuroComp2013,
title = {An input–output hidden Markov model for tree transductions},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro },
url = {http://www.sciencedirect.com/science/article/pii/S0925231213001914},
doi = {10.1016/j.neucom.2012.12.044},
issn = {0925-2312},
year = {2013},
date = {2013-01-01},
journal = {Neurocomputing},
volume = {112},
pages = {34--46},
keywords = {hidden Markov models, hidden tree Markov model, structured data processing, tree transductions},
pubstate = {published},
tppubtype = {article}
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2012
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees Journal Article
In: Neural Networks and Learning Systems, IEEE Transactions on, vol. 23, no. 12, pp. 1987 -2002, 2012, ISSN: 2162-237X.
Links | BibTeX | Tags: hidden Markov models, hidden tree Markov model, tree structured data
@article{gmtsdI2012,
title = {Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6353263},
doi = {10.1109/TNNLS.2012.2222044},
issn = {2162-237X},
year = {2012},
date = {2012-12-01},
journal = {Neural Networks and Learning Systems, IEEE Transactions on},
volume = {23},
number = {12},
pages = {1987 -2002},
keywords = {hidden Markov models, hidden tree Markov model, tree structured data},
pubstate = {published},
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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. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Input-Output Hidden Markov Models for Trees Conference
ESANN 2012 - The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings, Ciaco scrl - i6doc.com, 2012.
Abstract | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, structured data processing, tree structured data, tree transductions
@conference{11568_152836,
title = {Input-Output Hidden Markov Models for Trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
year = {2012},
date = {2012-01-01},
booktitle = {ESANN 2012 - The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings},
pages = {25--30},
publisher = {Ciaco scrl - i6doc.com},
abstract = {The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data pro- cessing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discrimina- tive structural information than non-input-driven approaches.},
keywords = {generative model, graphical models, hidden tree Markov model, structured data processing, tree structured data, tree transductions},
pubstate = {published},
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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 with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data pro- cessing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discrimina- tive structural information than non-input-driven approaches.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}
}
2010
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
A Bottom-up Hidden Tree Markov Model Technical Report
Università di Pisa no. TR-10-08, 2010.
Links | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, structured data processing, tree structured data
@techreport{11568_254437,
title = {A Bottom-up Hidden Tree Markov Model},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://compass2.di.unipi.it/TR/Files/TR-10-08.pdf.gz},
year = {2010},
date = {2010-04-01},
volume = {TR-10-08},
number = {TR-10-08},
pages = {1--22},
institution = {Università di Pisa},
keywords = {generative model, graphical models, hidden tree Markov model, structured data processing, tree structured data},
pubstate = {published},
tppubtype = {techreport}
}
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Bottom-Up Generative Modeling of Tree-Structured Data Conference
LNCS 6443: Neural Information Processing. Theory and Algorithms. Part I, vol. 6443, Springer-Verlag, BERLIN HEIDELBERG, 2010.
Links | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, structured data processing, tree structured data
@conference{11568_142187,
title = {Bottom-Up Generative Modeling of Tree-Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1007/978-3-642-17537-4_80},
year = {2010},
date = {2010-01-01},
booktitle = {LNCS 6443: Neural Information Processing. Theory and Algorithms. Part I},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {6443},
pages = {660--668},
publisher = {Springer-Verlag},
address = {BERLIN HEIDELBERG},
keywords = {generative model, graphical models, hidden tree Markov model, structured data processing, tree structured data},
pubstate = {published},
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Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Compositional Generative Mapping of Structured Data Conference
Proceedings of the 2010 IEEE InternationalJoint Conference on Neural Networks(IJCNN'10), IEEE, 2010.
Links | BibTeX | Tags: generative topographic mapping, graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning
@conference{11568_136433,
title = {Compositional Generative Mapping of Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/IJCNN.2010.5596606},
year = {2010},
date = {2010-01-01},
booktitle = {Proceedings of the 2010 IEEE InternationalJoint Conference on Neural Networks(IJCNN'10)},
pages = {1359--1366},
publisher = {IEEE},
keywords = {generative topographic mapping, graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}