@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466869,

title = {Model-based and model-free clustering: a case study of protein expression data for breast cancer},

author = {Lisboa Paulo JG and Jarman Ian H and Etchells Terence A and Bacciu Davide and Garibaldi John M},

year  = {2009},

date = {2009-01-01},

booktitle = {PROCEEDINGS OF THE 2009 UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465485,

title = {p-Health in Breast Oncology: A Framework for Predictive and Participatory e-Systems},

author = { Fernandes Ana S and Bacciu Davide and Jarman Ian H and Etchells Terence A and Fonseca Jose M and Paulo J G Lisboa},

doi = {10.1109/DeSE.2009.68},

year  = {2009},

date = {2009-01-01},

booktitle = {2009 Second International Conference on Developments in eSystems Engineering},

pages = {123--129},

publisher = {IEEE},

abstract = {Maintaining the financial sustainability of healthcare provision makes developments in e-Systems of the utmost priority in healthcare. In particular, it leads to a radical review of healthcare delivery for the future as personalised, preventive, predictive and participatory, or p-Health. It is a vision that places e-Systems at the core of healthcare delivery, in contrast to current practice. This view of the demands of the 21st century sets an agenda that builds upon advances in engineering devices and computing infrastructure, but also computational intelligence and new models for communication between healthcare providers and the public. This paper gives an overview of p-Health with reference to decision support in breast cancer.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465484,

title = {Patient stratification with competing risks by multivariate Fisher distance},

author = {Bacciu Davide and Jarman Ian H and Etchells Terence A and Lisboa Paulo J G},

doi = {10.1109/IJCNN.2009.5179077},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

booktitle = {2009 International Joint Conference on Neural Networks},

pages = {3453--3460},

publisher = {IEEE},

abstract = {Early characterization of patients with respect to their predicted response to treatment is a fundamental step towards the delivery of effective, personalized care. Starting from the results of a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD), we discuss an effective semi-supervised approach to patient stratification with application to Acute Myeloid Leukaemia (AML) data (n = 509) acquired prospectively by the GIMEMA consortium. Multiple prognostic indices provided by the survival model are exploited to build a metric based on the Fisher information matrix. Cluster number estimation is then performed in the Fisher-induced affine space, yielding to the discovery of a stratification of the patients into groups characterized by significantly different mortality risks following induction therapy in AML. The proposed model is shown to be able to cluster the input data, while promoting specificity of both target outcomes, namely Complete Remission (CR) and Induction Death (ID). This generic clustering methodology generates an affine transformation of the data space that is coherent with the prognostic information predicted by the PLANNCR-ARD model.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466669,

title = {Fuzzy Admission Control with Similarity Evaluation for VoWLAN with QoS Support},

author = {Bacciu Davide and Botta Alessio and Badia Leonardo },

doi = {10.1109/WONS.2008.4459355},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 Fifth Annual Conference on Wireless on Demand Network Systems and Services},

pages = {57--64},

publisher = {IEEE},

abstract = {In this paper, we make use of a fuzzy approach to determine a soft Admission Control mechanism for Voice-over-Internet-Protocol services over Wireless Local Area Network. In such a system, complicated interactions between service provider and clients take place, since the network capacity constraints must be matched with users' preferences and needs. Most of the difficulties in dealing with these interactions stem from the fact that it is very difficult to define both the load condition of the network and the users' requirements in a crisp manner. To this end, we define a framework in which the provider expresses the network status and the clients describe their preferences by means of an approach based on Fuzzy Set Theory. In this way, we are able to develop an Admission Control strategy, based on Similarity Evaluation techniques, that enforces the soft constraints expressed by the two parties. The obtained framework is numerically evaluated, showing the benefit of employing Fuzzy Set Theory with respect to the traditional crisp approach.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466667,

title = {Convergence Behavior of Competitive Repetition-Suppression Clustering},

author = {Bacciu Davide and Starita Antonina },

doi = {10.1007/978-3-540-69158-7_52},

year  = {2008},

date = {2008-01-01},

booktitle = {Neural Information Processing, Lecture Notes in Computer Science},

volume = {4984},

pages = {497--506},

publisher = {Springer},

abstract = {Competitive Repetition-suppression (CoRe) clustering is a bio-inspired learning algorithm that is capable of automatically determining the unknown cluster number from the data. In a previous work it has been shown how CoRe clustering represents a robust generalization of rival penalized competitive learning (RPCL) by means of M-estimators. This paper studies the convergence behavior of the CoRe model, based on the analysis proposed for the distance-sensitive RPCL (DSRPCL) algorithm. Furthermore, it is proposed a global minimum criterion for learning vector quantization in kernel space that is used to assess the correct location property for the CoRe algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465487,

title = {Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics},

author = {BACCIU Davide and BIGANZOLI Elia and LISBOA Paulo JG and Starita Antonina},

doi = {10.1007/978-3-540-85565-1-23},

year  = {2008},

date = {2008-01-01},

booktitle = {Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08)},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {5178},

pages = {181--188},

publisher = {Springer},

abstract = {A novel neural network clustering algorithm, CoRe, is benchmarked against previously published results on a breast cancer data set and applying the method of Partition Around Medoids (PAM). The data serve to compare the samples partitions obtained with the neural network, PAM and model-based algorithms, namely Gaussian Mixture Model (GMM), Variational Bayesian Gaussian Mixture (VBG) and Variational Bayesian Mixtures with Splitting (VBS). It is found that CoRe, on the one hand, agrees with the previously published partitions; on the other hand, it supports the existence of a supplementary cluster that we hypothesize to be an additional tumor subgroup with respect to those previously identified by PAM},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466674,

title = {A Fuzzy Approach for Negotiating Quality of Services},

author = {Bacciu Davide and Botta Alessio and Melgratti Hernan },

doi = {10.1007/978-3-540-75336-0_13},

year  = {2007},

date = {2007-01-01},

booktitle = {TRUSTWORTHY GLOBAL COMPUTING, Lecture Notes in Computer Science},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {4661},

pages = {200--217},

publisher = {Springer Verlag},

abstract = {A central point when integrating services concerns to the description, agreement and enforcement of the quality aspect of service interaction, usually known as Service Level Agreement (SLA). This paper presents a framework for SLA negotiation based on fuzzy sets. We propose (i) a request language for clients to describe quality preferences, (ii) a publication language for providers to define the qualities of their offered services, and (iii) a decision procedure for granting any client request with a SLA contract fitting the requestor requirements. We start with a restricted framework in which the different qualities of a service are handled independently (as being orthogonal) and then we propose an extension that allows clients and providers to express dependencies among different qualities.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466675,

title = {Fuzzy Agreement for Network Service Contracts},

author = {BACCIU Davide and BADIA Leonardo and BOTTA Alessio},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings of the 6th International Conference on Computational Intelligence in Economics & Finance (CIEF 2007)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466673,

title = {Augmenting the Distributed Evaluation of Path Queries via Information Granules},

author = {BACCIU Davide and BOTTA Alessio and STEFANESCU Dan},

url = {http://mlg07.dsi.unifi.it/pdf/16_Botta.pdf},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings of the 5th International Workshop on Mining and Learning with Graphs (MLG'07)},

pages = {105--109},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466672,

title = {A framework for semantic querying of distributed data-graphs via information granules},

author = {BACCIU Davide and BOTTA Alessio and STEFANESCU Dan},

url = {https://pages.di.unipi.it/bacciu/wp-content/uploads/sites/12/2016/04/bbs_ISC07.pdf

http://dl.acm.org/citation.cfm?id=1647449.1647477},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control},

pages = {161--166},

publisher = {ACTA PRESS},

address = {Anaheim, CA, USA},

abstract = {Regular path queries (RPQ) represent a common and convenient way to access and extract knowledge represented as labeled and weighted data-graphs. In this paper, we look to enhance the information representation in data-graphs and RPQs by augmenting their expressive power with the use of semantically meaningful knowledge in the form of information granules. We extended a recent distributed algorithm for the evaluation of RPQs on spatial networks by introducing fuzzy weights in place of crisp values both in the data-graphs and the query formulation. Moreover, we describe two alternative strategies for determining the costs of the paths computed by the fuzzy RPQ evaluation process. A spatial network case-study is used to illustrate the soundness of the approach.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466670,

title = {A Robust Bio-Inspired Clustering Algorithm for the Automatic Determination of Unknown Cluster Number},

author = {Bacciu Davide and Starita Antonina },

doi = {10.1109/IJCNN.2007.4371148},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

booktitle = {2007 International Joint Conference on Neural Networks},

pages = {1314--1319},

publisher = {IEEE},

abstract = {The paper introduces a robust clustering algorithm that can automatically determine the unknown cluster number from noisy data without any a-priori information. We show how our clustering algorithm can be derived from a general learning theory, named CoRe learning, that models a cortical memory mechanism called repetition suppression. Moreover, we describe CoRe clustering relationships with Rival Penalized Competitive Learning (RPCL), showing how CoRe extends this model by strengthening the rival penalization estimation by means of robust loss functions. Finally, we present the results of simulations concerning the unsupervised segmentation of noisy images.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_116977,

title = {Simultaneous clustering and feature ranking by competitive repetition suppression learning with application to gene data analysis},

author = {BACCIU Davide and MICHELI Alessio and STARITA Antonina},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED 2007)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466676,

title = {Competitive Repetition-suppression (CoRe) Learning},

author = {Bacciu Davide and Starita Antonina },

doi = {10.1007/11840817_14},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

booktitle = {ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 1, Lecture Notes in Computer Science},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {4131},

pages = {130--139},

publisher = {Springer Verlag},

abstract = {The paper introduces Competitive Repetition-suppression (CoRe) learning, a novel paradigm inspired by a cortical mechanism of perceptual learning called repetition suppression. CoRe learning is an unsupervised, soft-competitive [1] model with conscience [2] that can be used for self-generating compact neural representations of the input stimuli. The key idea underlying the development of CoRe learning is to exploit the temporal distribution of neurons activations as a source of training information and to drive memory formation. As a case study, the paper reports the CoRe learning rules that have been derived for the unsupervised training of a Radial Basis Function network.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466877,

title = {Assessment of stroke patients by whole-body isometric force-torque measurements II: software design of the ALLADIN Diagnostic Device},

author = {Cinkelj J and Mihelj M and Bacciu Davide and Jurak M and Guglielmelli Eugenio and Toth A and De Lafonteyne J and Verschelde J and Mazzoleni S and Van Vaerenbergh J and Ruijter S D and Munih M },

year  = {2005},

date = {2005-01-01},

booktitle = {Proceedings of the 3rd European Medical and Biological Engineering Conference},

journal = {IFMBE PROCEEDINGS (CD)},

volume = {1},

publisher = {IFMBE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466876,

title = {A RLWPR network for learning the internal model of an anthropomorphic robot arm},

author = {Bacciu Davide and Zollo Loredana and Guglielmelli Eugenio and Leoni Fabio and Starita Antonina},

doi = {10.1109/IROS.2004.1389362},

year  = {2004},

date = {2004-01-01},

urldate = {2004-01-01},

booktitle = {Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

volume = {1},

pages = {260--265},

publisher = {IEEE},

abstract = {Studies of human motor control suggest that humans develop internal models of the arm during the execution of voluntary movements. In particular, the internal model consists of the inverse dynamic model of the muscolo-skeletal system and intervenes in the feedforward loop of the motor control system to improve reactivity and stability in rapid movements. In this paper, an interaction control scheme inspired by biological motor control is resumed, i.e. the coactivation-based compliance control in the joint space and a feedforward module capable of online learning the manipulator inverse dynamics is presented. A novel recurrent learning paradigm is proposed which derives from an interesting functional equivalence between locally weighted regression networks and lakagi-Sugeno-Kang fuzzy systems. The proposed learning paradigm has been named recurrent locally weighted regression networks and strengthens the computational power of feedforward locally weighted regression networks. Simulation results are reported to validate the control scheme.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}