Fabio Aiolli

Research Area:	Machine Learning

University:	Padova
Department:	Matematica Pura ed Applicata
Address:	Via Belzoni, 7
	35131 Padova

Tel.:	049 827 5972
Fax:	049 827 5892
e-mail:	aiolli@math.unipd.it
Office:	06 ground floor, Plesso Paolotti

Teachings:

Informatica per Biologia 2005/2006

Research:

SVMs and Kernel-based Algorithms
Boosting
On-line Algorithms
Tangent Distance

Publications::

Journals
F. Aiolli, A. Sperduti	Multiclass Classification with Multi-Prototype Support Vector Machines Journal of Machine Learning Research, 6(May) 2005 - pages 817-850.
F. Aiolli, A. Sperduti	A re-weighting strategy for improving margins AI Journal, 2002 (137) - pages 197-216.
Conferences
F. Aiolli	A Preference Model for Structured Supervised Learning Tasks Proceedings of the ICDM Conference, New Orleans (Louisiana, USA) 2005
F. Aiolli, A. Sperduti	Learning Preferences for Multiclass Problems Proceedings of the NIPS Conference, Vancouver Canada 2004.
F. Aiolli, A. Sperduti	Multi-prototype Support Vector Machines Proceedings of the IJCAI Conference, Acapulco Mexico 2003.
F. Aiolli, F. Portera, A. Sperduti	Speeding up the solution of multi-label problems with Support Vector Machines Supplementary Proceedings of the ICANN/ICONIP Turkey, 2003.
F. Aiolli, A. Sperduti	An Efficient SMO-like algorithm for Multiclass SVM Proceedings of the NNSP, 2002.
F. Aiolli, A. Sperduti	A simple additive re-weighting scheme for improving margins Proceedings of the IJCAI Conference, Seattle USA 2001.

Other Publication

BIZON-IST Consortium, BIZON business requirements
E-COMM-LINE 2002 Conference in Bucharest, 2002.
BIZON-IST Consortium, Semi-Automatic Annotation in E-business Applications
E-COMM-LINE 2002 Conference in Bucharest, 2002.
F. Aiolli, M. Simi, D. Sona, A. Sperduti, A. Starita, G. Zaccagnini, SPI: A System for Paleographic Inspections, Notiziario AI*IA, N. 4, December 1999.
F. Aiolli, Sviluppo di un sistema basato sulla Distanza Tangente per l'Analisi Morfologica di Caratteri Paleografici, Master Thesis, February 1999.

PhD Thesis

Large Margin Multiclass Learning: Models and Algorithms. Dept. of. Computer Science, University of Pisa

Master Thesis

Sviluppo di un sistema basato sulla distanza tangente per l'analisi morfologica di caratteri paleografici (A Tangent Distance based system for morphological analysis of paleographic characters), Dept. of. Computer Science, University of Pisa

During my master thesis I applied Tangent Distance based algorithms to the Paleographic field with very promising results.
Personally, I implemented SPI (A System for Palegraphic Inspections), an integrated system that applies innovative segmentation and supervised learning techniques to the problem of paleographic document retrieval.

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Last modified:
Sep 30, 2005.