@conference {Dragoni2011541,
	title = {A continuos learning for a face recognition system},
	booktitle = {ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence},
	volume = {1},
	year = {2011},
	note = {cited By 0},
	pages = {541-544},
	abstract = {A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has a reliability factor defined as the probability that the network is giving the desired output. In case of conflicts between the outputs of the networks the reliability factor can be dynamically re-evaluated on the base of the Bayes Rule. The new reliabilities will be used to establish who is the subject. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960150495\&partnerID=40\&md5=4a12ff2305013f4969b4fc0242f2b7ec},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@article {Dragoni201179,
	title = {A continuous learning in a changing environment},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6979 LNCS},
	number = {PART 2},
	year = {2011},
	note = {cited By 0},
	pages = {79-88},
	abstract = {We propose a Hybrid System for dynamic environments, where a "Multiple Neural Networks" system works with Bayes Rule to solve a face recognition problem. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. For this purpose, we assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used to establish who is the conflict winner, making the final choice (the name of subject). Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning. {\textcopyright} 2011 Springer-Verlag.},
	doi = {10.1007/978-3-642-24088-1_9},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052998017\&partnerID=40\&md5=5004a58b7f29764e0e8129d5c456e8b9},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@article {Dragoni2011121,
	title = {Face recognition system in a dynamical environment},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6692 LNCS},
	number = {PART 2},
	year = {2011},
	note = {cited By 0},
	pages = {121-128},
	abstract = {We propose a Hybrid System for dynamic environments, where a "Multiple Neural Networks" system works with Bayes Rule to solve the face recognition problem. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. For this purpose, we assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used to establish who is the conflict winner, making the final choice. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning. {\textcopyright} 2011 Springer-Verlag.},
	doi = {10.1007/978-3-642-21498-1_16},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79957967237\&partnerID=40\&md5=ef902ad97692ff2d6ae546f1a63764bb},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@conference {Dragoni2010185,
	title = {Hybrid System for a never-ending unsupervised learning},
	booktitle = {2010 10th International Conference on Hybrid Intelligent Systems, HIS 2010},
	year = {2010},
	note = {cited By 0},
	pages = {185-190},
	abstract = {We propose a Hybrid System for dynamic environments, where a "Multiple Neural Networks" system works with Bayes Rule. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying two algorithms, the "Inclusion based" and the "Weighted" one over all the maximally consistent subsets of the global outcome. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/HIS.2010.5601070},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650122885\&partnerID=40\&md5=c1f15dbf9914a0b618932ba197795fa9},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@article {Dragoni2010296,
	title = {An hybrid system for continuous learning},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6077 LNAI},
	number = {PART 2},
	year = {2010},
	note = {cited By 0},
	pages = {296-303},
	abstract = {We propose a Multiple Neural Networks system for dynamic environments, where one or more neural nets could no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying two algorithms, the Inclusion based and the Weighted one over all the maximally consistent subsets of the global outcome. {\textcopyright} 2010 Springer-Verlag.},
	doi = {10.1007/978-3-642-13803-4_37},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77954587081\&partnerID=40\&md5=623ab72ce6e0ef83d2087d387550b607},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri and Mazzieri, M.}
}
@conference {Dragoni2010421,
	title = {Multiple Neural Networks and Bayesian belief revision for a never-ending unsupervised learning},
	booktitle = {Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA{\textquoteright}10},
	year = {2010},
	note = {cited By 0},
	pages = {421-426},
	abstract = {A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has a reliability factor defined as the probability that the network is giving the desired output. In case of conflicts between the outputs of the networks the reliability factor can be dynamically re-evaluated on the base of the Bayes Rrule. The new reliabilities will be used to establish who is the subject. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/ISDA.2010.5687229},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79851492270\&partnerID=40\&md5=51abe7b48e1259d0bbdbd2e8c498e0b4},
	author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri}
}
@conference {Dragoni2009164,
	title = {Multiple neural networks system for dynamic environments},
	booktitle = {ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications},
	year = {2009},
	note = {cited By 0},
	pages = {164-168},
	abstract = {We propose a "Multiple Neural Networks" system for dynamic environments, where one or more neural nets may no longer be able to properly operate, due to sensible partial changes in the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s "degree of reliability" is defined as "the probability that the net is giving the desired output", in case of conflicts between the outputs of the various nets the re-evaluation of their "degrees of reliability" can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying the "Inclusion based" algorithm over all the maximally consistent subsets of the global outcome. Finally, the nets recognized as responsible for the conflicts will be automatically forced to learn about the changes in the individuals{\textquoteright} characteristics and avoid to make the same error in the immediate future. {\textcopyright} 2009 IEEE.},
	doi = {10.1109/ISDA.2009.85},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77949498622\&partnerID=40\&md5=1b4b7bdf4786549e90ad4ae17f59a011},
	author = {Dragoni, A.F. and Paola Baldassarri and Vallesi, G. and Mazzieri, M.}
}
@conference {Iacopino2008364,
	title = {Artificial neural networks based symbolic gesture interface},
	booktitle = {SIGMAP 2008 - Proceedings of the International Conference on Signal Processing and Multimedia Applications},
	year = {2008},
	note = {cited By 0},
	pages = {364-369},
	abstract = {The purpose of the developed system is the realization of a gesture recognizer, applied to a user interface. We tried to get fast and easy software for user, without leaving out reliability and using instruments available to common user: a PC and a webcam. The gesture detection is based on well-known artificial vision techniques, as the tracking algorithm by Lucas and Kanade. The paths, opportunely selected, are recognized by a double layered architecture of multilayer perceptrons. The realized system is efficiency and has a good robustness, paying attention to an adequate learning of gesture vocabulary both for the user and for system.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-55849128216\&partnerID=40\&md5=ddfd2afde9d99702ec8cad5b4c8a5656},
	author = {Iacopino, C. and A. Montesanto and Paola Baldassarri and Dragoni, A.F. and Paolo Puliti}
}
@conference {Montesanto2008356,
	title = {Capturing the human action semantics using a query-by-example},
	booktitle = {SIGMAP 2008 - Proceedings of the International Conference on Signal Processing and Multimedia Applications},
	year = {2008},
	note = {cited By 0},
	pages = {356-363},
	abstract = {The paper describes a method for extracting human action semantics in video{\textquoteright}s using queries-by-example.b Here we consider the indexing and the matching problems of content-based human motion data retrieval. The query formulation is based on trajectories that may be easily built or extracted by following relevant points on a video, by a novice user too. The so realized trajectories contain high value of action semantics. The semantic schema is built by splitting a trajectory in time ordered sub-sequences that contain the features of extracted points. This kind of semantic representation allows reducing the search space dimensionality and, being human-oriented, allows a selective recognition of actions that are very similar among them. A neural network system analyzes the video semantic similarity, using a two-layer architecture of multilayer perceptrons, which is able to learn the semantic schema of the actions and to recognize them.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-55849129652\&partnerID=40\&md5=a8587b1b9833a9ccf64510cc75e426ab},
	author = {A. Montesanto and Paola Baldassarri and Dragoni, A.F. and Vallesi, G. and Paolo Puliti}
}
