{"id":422,"date":"2015-03-23T13:52:03","date_gmt":"2015-03-23T12:52:03","guid":{"rendered":"http:\/\/aixia2015.unife.it\/?page_id=422"},"modified":"2019-07-04T11:57:01","modified_gmt":"2019-07-04T10:57:01","slug":"mldm","status":"publish","type":"page","link":"https:\/\/aixia2015.unife.it\/events\/mldm\/","title":{"rendered":"4th Italian Workshop on Machine Learning and Data Mining (MLDM.it 2015)"},"content":{"rendered":"
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4th<\/span> Italian Workshop on Machine Learning and Data Mining (MLDM.it 2015)<\/h3>\n

Following the success of the first three editions of the Italian Workshop on Machine Learning and Data Mining at the AI*IA Symposiums and AI*IA Conferences on Artificial Intelligence, this workshop aims at bringing together researchers actively involved in the fields of machine learning, data mining, pattern recognition, and knowledge discovery.
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\nDuring the workshop, researchers will have the opportunity to present their recent results and discuss problems and challenges relevant to the community. Following the tradition of MLDM.it, presentations are upon invitation. Attendance to MLDM is open to all the AI*IA Conference participants.<\/p>\n

The meeting is organized by the AI*IA Working Group on Machine Learning and Data Mining whose general goal is to promote Italian scientific activities in the field of machine learning and data mining, and foster collaborations between research groups.<\/p>\n

Workshop Day: September\u00a022nd<\/span>, 2015<\/strong><\/p>\n

Program<\/h2>\n

MLDM.it will be held in Main Room<\/strong> - First Floor, Palazzo Trotti Mosti<\/em> (Trotti Mosti Palace): Corso Ercole I d'Este, 37 - Building Plan<\/a><\/p>\n

The program is downloadable here<\/a>.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 <\/th>\n22 September 2015<\/strong><\/th>\n <\/th>\n<\/tr>\n<\/thead>\n
<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
8.50-9.00<\/td>\nOpening<\/em><\/td>\n<\/td>\n<\/tr>\n
9.00-9.10<\/td>\nWelcome<\/em><\/td>\n<\/td>\n<\/tr>\n
Section I<\/strong><\/td>\nComplex Data Analysis<\/strong><\/td>\n<\/td>\n<\/tr>\n
9.10-9.30<\/td>\nMining Time-Adaptive Interpolative Clusters: Linking Interpolation to Summarization in Geophysical Data Streams<\/em><\/td>\nAnnalisa Appice, Donato Malerba<\/td>\n<\/tr>\n
9.30-9.50<\/td>\nVisual Detection of small groups and leaders in crowd through structured learning<\/em><\/td>\nSimone Calderara<\/td>\n<\/tr>\n
9.50-10.10<\/td>\nReal-Time Topic Detection in Twitter<\/em><\/td>\nSalvatore Gaglio, Giuseppe Lo Re, Marco Morana<\/td>\n<\/tr>\n
10.10-10.30<\/td>\nChoice of Training-Validation partitions impacts predictive performance<\/em><\/td>\nAlessandro Zandon\u00e0, Marco Chierici, Giuseppe Jurman, Cesare Furlanello<\/td>\n<\/tr>\n
Coffee break<\/strong><\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Section II<\/strong> <\/td>\nDeep Learning<\/strong><\/td>\n<\/td>\n<\/tr>\n
11.00-11.20<\/td>\nTwo approaches for Recurrent Neural Networks Pre-training<\/em><\/td>\nLuca Pasa, Alessandro Sperduti, Alberto Testolin<\/td>\n<\/tr>\n
11.20-11.40<\/td>\nOn the Complexity of Neural Network Classifiers: A Comparison between Shallow and Deep Architectures<\/em><\/td>\nMonica Bianchini, Franco Scarselli<\/td>\n<\/tr>\n
11.40-12.00<\/td>\nDeep Reservoir Computing<\/em><\/td>\nClaudio Gallicchio, Alessio Micheli<\/td>\n<\/tr>\n
12.00-12.20<\/td>\nTweaking Sum-Product Network Structure Learning<\/em><\/td>\nAntonio Vergari, Nicola Di Mauro, Floriana Esposito<\/td>\n<\/tr>\n
Lunch break<\/strong><\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Section III<\/strong><\/td>\nLearning Models and Analysis (I)<\/strong><\/td>\n<\/td>\n<\/tr>\n
14.30-14.50<\/td>\nDistributed Probabilistic Logic Learning<\/em><\/td>\nElena Bellodi, Giuseppe Cota, Evelina Lamma, Fabrizio Riguzzi, Riccardo Zese<\/td>\n<\/tr>\n
14.50-15.10<\/td>\nTruly scalable latent locally linear SVMs<\/em><\/td>\nIlja Kuzborskij, Barbara Caputo<\/td>\n<\/tr>\n
15.10-15.30<\/td>\nA model of online learning as a Linear Quadratic Gaussian (LQG) optimal control problem with random matrices<\/em><\/td>\nGiorgio Gnecco, Alberto Bemporad, Marco Gori, Rita Morisi, and Marcello Sanguineti<\/td>\n<\/tr>\n
15.30-15.50<\/td>\nGrammatical Inference for Structural Knowledge Extraction<\/em><\/td>\nPietro Cottone, Salvatore Gaglio, Giuseppe Lo Re, and Marco Ortolani<\/td>\n<\/tr>\n
Coffee break<\/strong><\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Section IV<\/strong><\/td>\nLearning Models and Analysis (II)<\/strong><\/td>\n<\/td>\n<\/tr>\n
16.30-16.50<\/td>\nRNAsynth: a graph kernel approach to learn constraints for RNA inverse folding<\/em><\/td>\nFabrizio Costa<\/td>\n<\/tr>\n
16.50-17.10<\/td>\nTransducing Sentences to Syntactic Feature Vectors: an Alternative Way to \u201cParse\u201d?<\/em><\/td>\nFabio Massimo Zanzotto, Lorenzo Dell\u2019Arciprete, Lorenzo Ferrone<\/td>\n<\/tr>\n
17.10-17.30<\/td>\nConceptual change as a cognitive phase transition<\/em><\/td>\nLorenza Saitta<\/td>\n<\/tr>\n
17.30-17.45<\/td>\nModel complexities of shallow neural networks for the approximation of input-output mappings with large variations<\/em><\/td>\nMarcello Sanguineti, Vera Kurkova<\/td>\n<\/tr>\n
17.45-18.00<\/td>\nDiscussion and Conclusions: The future of MLDM<\/em><\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p>\n

Organising Committee<\/h2>\n

Alessio Micheli<\/a>
\nUniversity of Pisa<\/p>\n

Giorgio Valentini<\/a>
\nUniversity of Milano<\/p>\n

Advisory board<\/h2>\n

Nicol\u00f2 Cesa-Bianchi
\nPaolo Frasconi
\nAlessio Micheli
\nMarcello Pelillo
\nGiorgio Valentini
\nAlessandro Verri<\/p>\n