59 entries « ‹ 2 of 2
› » 2014
51.
Davide, Bacciu; Paolo, Barsocchi; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli
An experimental characterization of reservoir computing in ambient assisted living applications Journal Article
In: Neural Computing and Applications, vol. 24, no. 6, pp. 1451-1464, 2014, ISSN: 0941-0643.
@article{nca2014,
title = {An experimental characterization of reservoir computing in ambient assisted living applications},
author = {Bacciu Davide and Barsocchi Paolo and Chessa Stefano and Gallicchio Claudio and Micheli Alessio},
url = {http://dx.doi.org/10.1007/s00521-013-1364-4, Publisher version
https://archive.ics.uci.edu/ml/datasets/Indoor+User+Movement+Prediction+from+RSS+data, Dataset @ UCI},
doi = {10.1007/s00521-013-1364-4},
issn = {0941-0643},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Neural Computing and Applications},
volume = {24},
number = {6},
pages = {1451-1464},
publisher = {Springer London},
abstract = {In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.
2013
52.
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.
@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},
volume = {24},
number = {2},
pages = {231 -247},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
53.
Davide, Bacciu; A, Etchells Terence; JG, Lisboa Paulo; Joe, Whittaker
Efficient identification of independence networks using mutual information Journal Article
In: Computational Statistics, vol. 28, no. 2, pp. 621-646, 2013, ISSN: 0943-4062.
@article{bgm2013,
title = {Efficient identification of independence networks using mutual information},
author = {Bacciu Davide and Etchells Terence A and Lisboa Paulo JG and Whittaker Joe},
url = {http://dx.doi.org/10.1007/s00180-012-0320-6},
doi = {10.1007/s00180-012-0320-6},
issn = {0943-4062},
year = {2013},
date = {2013-01-01},
journal = {Computational Statistics},
volume = {28},
number = {2},
pages = {621-646},
publisher = {Springer-Verlag},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
54.
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.
@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 = {},
pubstate = {published},
tppubtype = {article}
}
55.
Nicola, Di Mauro; Paolo, Frasconi; Fabrizio, Angiulli; Davide, Bacciu; de Gemmis Marco,; Floriana, Esposito; Nicola, Fanizzi; Stefano, Ferilli; Marco, Gori; A, Lisi Francesca; others,
Italian Machine Learning and Data Mining research: The last years Journal Article
In: Intelligenza Artificiale, vol. 7, no. 2, pp. 77–89, 2013.
@article{di2013italian,
title = {Italian Machine Learning and Data Mining research: The last years},
author = {Di Mauro Nicola and Frasconi Paolo and Angiulli Fabrizio and Bacciu Davide and de Gemmis Marco and Esposito Floriana and Fanizzi Nicola and Ferilli Stefano and Gori Marco and Lisi Francesca A and others},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6353263},
doi = {10.3233/IA-130050},
year = {2013},
date = {2013-01-01},
journal = {Intelligenza Artificiale},
volume = {7},
number = {2},
pages = {77--89},
publisher = {IOS Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2012
56.
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.
@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 = {},
pubstate = {published},
tppubtype = {article}
}
2011
57.
H, Jarman Ian; A, Etchells Terence; Davide, Bacciu; M, Garibaldi John; O, Ellis Ian; JG, Lisboa Paulo
Clustering of protein expression data: a benchmark of statistical and neural approaches Journal Article
In: Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 15, no. 8, pp. 1459–1469, 2011, ISSN: 1432-7643.
@article{soco2011,
title = {Clustering of protein expression data: a benchmark of statistical and neural approaches},
author = {Jarman Ian H and Etchells Terence A and Bacciu Davide and Garibaldi John M and Ellis Ian O and Lisboa Paulo JG},
url = {http://dx.doi.org/10.1007/s00500-010-0596-9},
doi = {10.1007/s00500-010-0596-9},
issn = {1432-7643},
year = {2011},
date = {2011-01-01},
journal = {Soft Computing-A Fusion of Foundations, Methodologies and Applications},
volume = {15},
number = {8},
pages = {1459--1469},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2009
58.
Davide, Bacciu; Antonina, Starita
Expansive competitive learning for kernel vector quantization Journal Article
In: Pattern Recognition Letters, vol. 30, no. 6, pp. 641–651, 2009, ISSN: 0167-8655.
@article{patrec2009,
title = {Expansive competitive learning for kernel vector quantization},
author = {Bacciu Davide and Starita Antonina},
url = {http://dx.doi.org/10.1016/j.patrec.2009.01.002},
doi = {10.1016/j.patrec.2009.01.002},
issn = {0167-8655},
year = {2009},
date = {2009-01-01},
journal = {Pattern Recognition Letters},
volume = {30},
number = {6},
pages = {641--651},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2008
59.
Davide, Bacciu; Antonina, Starita
Competitive Repetition Suppression (CoRe) Clustering: A Biologically Inspired Learning Model With Application to Robust Clustering Journal Article
In: Neural Networks, IEEE Transactions on, vol. 19, no. 11, pp. 1922 -1941, 2008, ISSN: 1045-9227.
@article{coreTNN2008,
title = {Competitive Repetition Suppression (CoRe) Clustering: A Biologically Inspired Learning Model With Application to Robust Clustering},
author = {Bacciu Davide and Starita Antonina},
url = {http://dx.doi.org/10.1016/j.patrec.2009.01.002},
doi = {10.1109/TNN.2008.2004407},
issn = {1045-9227},
year = {2008},
date = {2008-11-01},
urldate = {2008-11-01},
journal = {Neural Networks, IEEE Transactions on},
volume = {19},
number = {11},
pages = {1922 -1941},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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