@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 = {http://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}

}