Capitolo di Libri

G. Armano, A. Giuliani, A. Messina, M. Montagnuolo, and E. Vargiu, “Content-based Keywords Extraction and Automatic Advertisement Associations to Multimodal News Aggregations”, New Challenges in Distributed Information Filtering and Retrieval (DART 2011), Springer-Verlag, Studies in Computational Intelligence series, Vol. 439, pp. 33-52, 2013 (in press).

G. Armano, A. Giuliani, and E. Vargiu. “Intelligent Techniques in Recommender Systems and Contextual Advertising: Novel Approaches and Case Studies”, in Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods, S. Dehuri, M.R. Patra, B.B. Misra, A.K. Jagadev (eds.), IGI Global, pp. 105-128, doi: 10.4018/978-1-4666-2542-6, 2012.

A. Addis, G. Armano, E. Vargiu, and A. Manconi, “Retrieving and Categorizing Bioinformatics Publications through a MultiAgent System”, in Computational Biology and Applied Bioinformatics, H. S. Lopes and L.M. Cruz (ed.), InTech Publishing, Rijeka, Croatia, 2011.

A. Addis, G. Armano and E. Vargiu, “Progressive Filtering on the Web: The Press Reviews Case Study”, in Learning Structure and Schemas from Documents – Studies in Computational Intelligence, Springer-Verlag, Vol. 375, pp 143-163, 2011.

G. Armano and A. Manconi. “A Collaborative Web Application for Supporting Researchers in the Task of Generating Protein Datasets”, in Advances in Distributed Agent-based Retrieval Tools – Studies in Computational Intelligence, Springer-Verlag, Vol. 361, pp. 13-26, 2011.

G. Armano, F. Ledda and E. Vargiu. “SSP2: A Novel Software Architecture for Predicting Protein Secondary Structure”, in Sequence and Genome Analysis: Methods and Application, G. Fung (ed.), iConcept press, 2011.

G. Armano and N. Hatami, “An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes”, in Ensembles in Machine Learning Applications – Studies in Computational Intelligence, Vol. 373, pp. 217-231, Springer-Verlag, doi: 10.1007/978-3-642-22910-7_13, 2011.

G. Armano and N. Hatami, “Run-Time Performance Analysis of the Mixture of Experts Model”, in Computer Recognition Systems 4 – Advances in Intelligent and Soft Computing, Vol. 95, pp. 167-175, Springer-Verlag, doi: 10.1007/978-3-642-20320-6_18, 2011.

Hota C., Ledda F., Armano G., “A Resilient Voting Scheme for Improving Secondary Structure Prediction”, in Multi-disciplinary Trends in Artificial Intelligence, 5th International Workshop (MIWAI 2011), Hyderabad, India, December 7-9, 2011 (LNCS. Proceedings, Springer, pp. 339-350, ISBN 978-1-4419-7181-4).

G. Armano and E. Vargiu, “A MultiAgent System for Monitoring Boats in Marine Reserves", in Programming Multi-Agent Systems, L. Braubach, J.P. Briot, and J. Thangarajah (Eds.), LNAI series, Springer-Verlag, ISBN 978-3-642-14842-2, Vol. 5919, pp. 254-265, 2010.

G. Armano “NXCS Experts for Financial Time Series Forecasting”, in Applications of Learning Classifier Systems, Larry Bull (ed.), Springer, pp. 68-91, 2004.

G. Armano, M. Marchesi, and A. Murru, “NXCS: Hybrid Approach to Stock Indexes Forecasting”, in Genetic Algorithms and Genetic Programming in Computational Finance, Shu-Heng Chen (ed.), Part 2: Forecasting, Chapter 6, Kluwer, pp. 125-158, 2002.

G. Armano and M. Marchesi, “A Flexible Software Development Process for Emergent Organizations”, in eXtreme Programming Examined, G. Succi and M. Marchesi (eds.), The XP Series, Addison Wesley, May 2001.

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