Dario Malchiodi > Research activities

Research activities

My research activities focus around the treatment of uncertainty in machine learning problems, with the aim of strenghtening the aspects belonging to the fields of computer science and statistics.

Data quality-based learning

Machine learning models have as starting point a labeled sample whose elements are processed homogeneously (that is, each element has the same importance). In [Malchiodi, 2008] the general model of data quality-based learning was proposed. In this model it is possible to associate each of the available data items a numerical quantification of its importance with reference to the remaining data. This model was applied to the problem of classification through Support Vector Machines, both in its linear [Apolloni and Malchiodi, 2006a] and kernel-based version [Apolloni et al., 2007c]. A first analysis of the performance for these applications has been undertaken both theoretically [Apolloni et al., 2007d] and experimentally [Malchiodi, 2009]. Some preliminary applications in the bioinformatics field is described in [Malchiodi et al., 2010]. A similar approach has also been applied to the regression problem in [Apolloni et al., 2010; Malchiodi et al., 2009c; Apolloni et al., 2005b].

Analysis of relations between granular computing and machine learning

The granular computing model, giving information a granular meaning and allowing its analysis and its processing at different abstraction levels, is described in [Apolloni et al., 2008], where its links with machine learning models are analysed. The effects of a fusion of these two models have been studied within the general field of regression, proposing new algorithms based on Support Vector Machines [Apolloni et al., 2008a; Apolloni et al., 2006e] or on local search techniques [Apolloni et al., 2005b].

Bootstrap techniques for regression algorithms

Bootstrap techniques are based on data resampling models with the aim of approximating the distribution of a population. A specialization of this kind of techniques, intially proposed in [Apolloni et al., 2006] and subsequently refined in [Apolloni et al., 2009; Apolloni et al., 2007], gives as output confidence regions for regression curves, avoiding usual assumptions on the distribution of measurement drifts. The use of this technique to solve linear and nonlinear regression problems is shown in [Apolloni et al., 2008b], while [Apolloni et al., 2007b] describes some applications to the medical field.

Development of inference models for machine learning problems

The task of integrating under a unique theoretical model istances of inference problems from statistics (point and interval estimation of distribution parameters) and computer science (estimation of approximation error in machine learning) is tackled in [Apolloni et al., 2006; Apolloni et al., 2005d; Apolloni et al., 2002e; Apolloni et al., 2002d; Apolloni and Malchiodi, 2001a; Malchiodi, 2000], building on previously obtained results on sample complexity [Apolloni and Malchiodi, 2001] and describing the Algorithmic Inference model. This model was used with the aim of estimating the risk in classification problems based on Support Vector Machines [Apolloni et al., 2007d; Apolloni et al., 2005a; Apolloni and Malchiodi, 2002a; Apolloni and Malchiodi, 2001a], learning confidence regions for regression lines avoiding the typical assumption requiring a Gaussian drift distribution [Apolloni et al., 2005e; Apolloni et al., 2002f], and learning confidence regions for the risk function of re-occurrence distribution times in particular cancer pathologies [Apolloni et al., 2007b; Apolloni et al., 2005f; Apolloni et al., 2002i].

Applications of systems for scientific computation

Systems for scientific computation can be used to run simulations and to analyze mathematical problems from an interactive and incremental point of view; To this effect, such systems offer interesting cues in order to design educational activities [Bulgheroni and Malchiodi, 2009; Malchiodi, 2008a]. A commercial version of this kind of systems, thoroughly described in [Malchiodi, 2007], has been extended so as to solve purely computational aspects associated to information encoding [Malchiodi, 2006c], remote procedure invocation [Malchiodi, 2006b; Malchiodi, 2006], and solutions to optimization [Malchiodi, 2006a] and machine learning problems based on Support Vectors [Malchiodi et al., 2009b; Malchiodi et al., 2009a]. The related code has been used in order to build up the simulations in [Apolloni et al., 2007c; Apolloni and Malchiodi, 2006a]. Moreover, [Malchiodi, 2010a] describes a library handling machine learning problems within an open source system for scientific computation.

Design of hybrid learning systems

Hybrid learning systems are typically organized coupling sub-symbolic modules (typically based on the neural networks paradigm) with symbolic ones (described in terms of logic circuits). Such a system, having as inputs a set of features describing the available data and extracting their boolean independent components, is described in [Apolloni et al., 2005; Apolloni et al., 2004]. These components, interpreted as truth values, are used in order to infer logical formulas describing in a symbolic ways the relations among original input data [Apolloni et al., 2006a; Apolloni et al., 2003; Apolloni et al., 2002; Apolloni et al., 2000]. This system is applied in [Apolloni et al., 2004] to the problem of emotion recognition on the basis of voice signals, while [Apolloni et al., 2004b; Apolloni et al., 2004a; Apolloni et al., 2003c; Apolloni et al., 2003b; Apolloni et al., 2003a] describes an applications to the monitoring of awareness in car driving in function of biosignals, within the research project IST-2000-26091 ORESTEIA (mOdular hybRid artEfactS wiTh adaptivE functIonAlity, funded between 2001 and 2003 by the EC within the fifth framework programme, under the IST-FET initiative). Moreover, [Apolloni and Malchiodi, 2006a; Apolloni et al., 2005b] study two hybrid systems obtained through the integration of a fuzzy system for the measurement of quality in available data respectively with a linear Support Vector classifier and with a linear regression model.

Automatic simplification of symbolic descriptions

Whithin computational learning theory, the structural risk minimization principle investigates on the problem of balancing the complexity of a model with its accuracy in describing experimental data. This principle has been applied to classifiers based on logic expressions built in terms of disjuctive and conjunctive boolean normal forms. A simplification algorithm for such forms was developed in [Apolloni et al., 2006a; Apolloni et al., 2005g; Apolloni et al., 2003; Apolloni et al., 2002h; Apolloni et al., 2002b], focusing on the stochastic optimization of parameters in fuzzy sets describing the above mentioned forms.

Study of population dynamics

Within this subject the activities have been focused on the problem of modeling conflicting situations through an approach alternative to that of classical game theory. In particular, these conflicts were modeled in terms of approximating the solution to an NP-hard problem [Apolloni et al., 2006c; Apolloni et al., 2003d; Apolloni et al., 2002g; Apolloni et al., 2002c], applying the Algorithmic Inference model in order to assign limited computational resources to two players, subsequently extending this technique to team games [Apolloni et al., 2006b]. This model is applied in [Apolloni et al., 2007a; Apolloni et al., 2005c] to the biologic field, while [Apolloni et al., 2010a] uses this approach with the aim of correctly dimensioning the running time for learning algorithms based on local error minimization.

Intelligent systems for pervasive and ubiquitous computing

The research project ORESTEIA (mOdular hybRid artEfactS wiTh adaptivE functIonAlity, funded between 2001 and 2003 by the EC within the fifth framework programme, under the IST-FET initiative) was grounded on the design, implementation and analysis of intelligent systems for pervasive and ubiquitous computing. These fields are characterized by highly specialized computers devoted to execute specific tasks. These special computers can be produced so as to significantly reduce their size and cost, consequently being able to immerse them inside an environment. Focusing specifically on the awareness detection problem [Kasderidis et al., 2003], a prototype for the detection of driving awareness on the basis of biosignals [Apolloni et al., 2004b; Apolloni et al., 2004a; Apolloni et al., 2003c; Apolloni et al., 2003b; Apolloni et al., 2003a] have been developed.

Automatic classification of emotions

Within the progress of reserach project PHYSTA (Principled Hybrid Systems: Theory and Applications, funded between 1998 and 2000 by the EC within the fourth framework programme, within the TMR initiative), the Algorithmic Inference model described in [Apolloni et al., 2006; Malchiodi, 2000] was applied to the problem of automatic classification of emotions on the basis of vocal signals [Apolloni et al., 2004; Apolloni et al., 2002]. The obtained results were presented at an international school on computational learning within the same research project.

Design of hardware-implementable statistics

The availability of hardware circuits able to directly process information with the aim of synthesizing them through estimators allow a remarkable shortening in running times. Their use imply a set of constraints basically linked to the architecture of the circuits themselves. The inference-among-gossips, developed in [Malchiodi, 1996], has been applied within this scope with the aim of obtaining a family of estimators for bernoulli populations directly implementable on pRAM boards [Apolloni et al., 1997].

Membership to research groups

• PASCAL 2 network of excellence;
• Italian Society for Neural Networks;
• Italian Association of University Professors of Computer Science (GRIN);
• Neural Networks Laboratory at the Computer Science Department, Milan University.

Participation in research projects

2005 > 2008

Network of excellence PASCAL: Pattern Analysis, Statistical Modeling and Computational Learning, finanziata dall'EC;

2002 > 2004

Project Stochastic processes, funded by the Italian Ministry for University and Research

2001 > 2003

IST-FET research project ORESTEIA (mOdular hybRid artEfactS wTh adaptivE funtIonAlity, funded by the European Union under the fifth framework programme)

1998 > 2000

TMR research project PHYSTA (Principled Hybrid Sistems: Theory and Applications, funded by the European Union under the fourth framework programme)

2000

Project Statistical and Neural Methods supporting decisions in finance, funded under the grant Young Researchers at the Milan UniversityProgetto Metodi statistici e neurali di supporto alle decisioni in ambito finanziario, finanziato dal Inferentia-DNM

2000

Project Statistical and Neural Methods for population dynamics, funded under the grant Young Researchers at the Milan University

1099

Project Spatial stochastic processes, funded by the Italian Ministry for University and Research

Publications

Books

[Apolloni et al., 2008] B. Apolloni, W. Pedrycz, S. Bassis and D. Malchiodi, The Puzzle of Granular Computing, Springer, Studies in Computational Intelligence, Vol. 138 (ISBN 978-3-540-79863-7), 2008 [ publisher BIBTEX ]

[Malchiodi, 2007] D. Malchiodi, Fare matematica con Mathematica, Milano: Pearson Addison Wesley (ISBN 978-88-7192-365-9), 2007, in italian [ book-page publisher BIBTEX ]

[Apolloni et al., 2006] B. Apolloni, D. Malchiodi and S. Gaito, Algorithmic Inference in Machine Learning, 2nd Edition, Magill, Adelaide: Advanced Knowledge International, International Series on Advanced Intelligence, Vol. 5 (ISBN 0-9751004-2-4), 2006 [ publisher BIBTEX ]

Papers in international journals

[Apolloni et al., 2010] B. Apolloni, D. Malchiodi and L. Valerio, Relevance regression learning with support vector machines, Nonlinear Analysis 73 (2010), 2855-2867 [ doi> BIBTEX ]

[Apolloni et al., 2010a] B. Apolloni, S. Bassis, S. Gaito, D. Malchiodi and I. Zoppis, Playing monotone games to understand learning behaviors, Theoretical Computer Science 411 - 25 (2010), 2384-2405 [ doi> BIBTEX ]

[Apolloni et al., 2009] B. Apolloni, S. Bassis and D. Malchiodi, Compatible worlds, Nonlinear Analysis: Theory, Methods & Applications 71 - 12 (2009), e2883-e2901 [ doi> BIBTEX ]

[Malchiodi, 2009] D. Malchiodi, An experimental analysis of the impact of accuracy degradation in SVM classification, International Journal of Computational Intelligence Studies 1 - 2 (2009), 163-190 [ doi> BIBTEX ]

[Apolloni et al., 2008a] B. Apolloni, S. Bassis, D. Malchiodi and W. Pedrycz, Interpolating Support Information Granules, Neurocomputing 71 (2008), 2433-2445 [ doi> BIBTEX ]

[Apolloni et al., 2008b] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Bootstrapping Complex Functions, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 648-664 [ doi> BIBTEX ]

[Malchiodi, 2008] D. Malchiodi, Embedding Sample Points Uncertainty Measures in Learning Algorithms, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 635-647 [ doi> BIBTEX ]

[Apolloni et al., 2007] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Solving complex regression problems via Algorithmic Inference: a new family of bootstrap algorithms, Far East Journal of Theoretical Statistics 22 - 2 (2007), 141-180 [ BIBTEX ]

[Apolloni et al., 2007a] B. Apolloni, S. Bassis, A. Clivio, S. Gaito and D. Malchiodi, Modeling individual's aging within a bacterial population using a pi-calculus paradigm, Natural Computing 6 - 1 (2007), 33-53 [ doi> BIBTEX ]

[Apolloni et al., 2007b] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Appreciation of medical treatments by learning underlying functions with good confidence, Current Pharmaceutical Design 13 - 15 (2007), 1545-1570 [ BIBTEX ]

[Apolloni et al., 2006a] B. Apolloni, A. Brega, D. Malchiodi, G. Palmas and A. M. Zanaboni, Learning Rule Representations From Data, IEEE Transactions on Systems, Man and Cybernetics, Part A 36 - 5 (2006), 1010-1028 [ doi> BIBTEX ]

[Apolloni et al., 2006b] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Elementary team strategies in a monotone game, Nonlinear Analysis 64 - 2 (2006), 310-328 [ doi> BIBTEX ]

[Apolloni et al., 2006c] B. Apolloni, S. Bassis, S. Gaito, D. Malchiodi and I. Zoppis, Controlling the losing probability in a monotone game, Information Sciences 176 - 10 (2006), 1395-1416 [ doi> BIBTEX ]

[Apolloni et al., 2004] B. Apolloni, A. Esposito, D. Malchiodi, C. Orovas, G. Palmas and J. G. Taylor, A General Framework for Learning Rules From Data, IEEE Transactions on Neural Networks 15 - 6 (2004), 1333-1349 [ doi> BIBTEX ]

[Apolloni et al., 2002] B. Apolloni, D. Malchiodi, C. Orovas and G. Palmas, From synapses to rules, Cognitive Systems Research 3 (2002), 167-201 [ doi> BIBTEX ]

[Apolloni and Malchiodi, 2001] B. Apolloni and D. Malchiodi, Gaining degrees of freedom in subsymbolic learning, Theoretical Computer Science 255 (2001), 295-321 [ doi> BIBTEX ]

[Apolloni et al., 1997] B. Apolloni, D. Malchiodi and J. G. Taylor, Functional bootstrap: a hardware constrained implementation of on-line bootstrap, InterStat October (1997) [ on-line access BIBTEX ]

Papers in international conference proceedings

[Apolloni et al., 2007c] B. Apolloni, D. Malchiodi and L. Natali, A Modified SVM Classification Algorithm for Data of Variable Quality, in B. Apolloni, R. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part III, Berlin Heidelberg: Springer-Verlag, Lecture Notes in Artificial Intelligence 4694 (ISBN 978-3-540-74828-1), 131-139, 2007 [ doi> on-line access BIBTEX ]

[Apolloni et al., 2007d] B. Apolloni, S. Bassis and D. Malchiodi, SVM with Random Labels, in B. Apolloni, R. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part III, Berlin Heidelberg: Springer-Verlag, Lecture Notes in Artificial Intelligence 4694 (ISBN 978-3-540-74828-1), 184-193, 2007 [ doi> on-line access BIBTEX ]

[Apolloni and Malchiodi, 2006a] B. Apolloni and D. Malchiodi, Embedding sample points relevance in SVM linear classification, in V. Torra, Y. Narukawa, A. Valls and J. Domingo-Ferrer (Eds.), MDAI 2006 - Proceedings of 3rd International Conference on Modeling Decisions for Artificial Intelligence, Tarragona: Universitat Rovira I Virgili (ISBN 8400-08416-0), 2006 [ BIBTEX ]

[Apolloni et al., 2006e] B. Apolloni, S. Bassis, D. Malchiodi and W. Pedrycz, Interpolating Support Information Granules, in S. Kollias, A. Stafylopatis, W. Duch and E. Oja (Eds.), Artificial Neural Networks - ICANN 2006 - 16th International Conference, Athens, Greece, September 10-14, 2006, Proceedings, Part II, Berlin/Heidelberg: Springer, Lecture Notes in Computer Science 4132 (ISBN 978-3-540-38871-5), 270-281, 2006 [ doi> on-line access BIBTEX ]

[Malchiodi, 2006] D. Malchiodi, Implementing an XML-RPC client in Mathematica, in B. Autin and Y. Papegay (Eds.), eProceedings of the 8th International Mathematica Symposium, Rocquencourt, France: INRIA (ISBN 2-7261-1289-7), 2006 [ BIBTEX ]

[Apolloni et al., 2005] B. Apolloni, A. Brega and D. Malchiodi, BICA: a Boolean Independent Component Analysis Algorithm, in N. Nedjah, L. Mourelle, M. B. R. Vellasco, A. Abraham and M. Köppen (Eds.), Proceedings of HIS 2005: Fifth International Conference on Hybrid Intelligent Systems, IEEE Computer Society (ISBN 0-7695-2457-5), 131-136, 2005 [ doi> BIBTEX ]

[Apolloni et al., 2005a] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Tight Bounds for SVM Classification Error, in M. Zhao and Z. Shi (Eds.), Proceedings - 2005 International Conference on Neural Network & Brain (ICNN&B'05), IEEE Press (ISBN 0-7803-9422-4), 5-8, 2005 [ on-line access BIBTEX ]

[Apolloni et al., 2005f] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Appreciation of medical treatments through confidence intervals, in E. Biganzoli, P. Boracchi, P. Duca and E. Ifeachor (Eds.), Proceedings of the 1t European Workshop on the Assessment of Diagnostic Performance, RCE Edizioni (ISBN 88-8399-084-6), 165-174, 2005 [ BIBTEX ]

[Apolloni et al., 2004a] B. Apolloni, A. Brega, D. Malchiodi and C. Mesiano, Detecting Driving Awareness, in J. Boulicaut, F. Esposito, F. Giannotti and D. Pedreschi (Eds.), Knowledge Discovery in Databases - PKDD 2004. 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004. Proceedings, Berlin, Heidelberg: Springer, Lecture Notes in Artificial Intelligence 3202 (ISBN 3-540-23108-0), 528-530, 2004, demonstrating paper [ doi> online-access BIBTEX ]

[Apolloni et al., 2004b] B. Apolloni, D. Malchiodi and C. Mesiano, An Attention Monitoring System for High Demanding Operational Tasks, in Proceedings of the 2004 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, IEEE Press (ISBN 0-7803-8381-8), 23-29, 2004, invited paper [ doi> BIBTEX ]

[Apolloni et al., 2003] B. Apolloni, A. Brega, D. Malchiodi, G. Palmas and A. M. Zanaboni, Learning rule representations from boolean data, in O. Kaynak, E. Alpaydin, E. Oja and L. Xu (Eds.), Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings, Springer, Lecture Notes in Computer Science 2714, 875-882, 2003 [ doi> on-line access BIBTEX ]

[Apolloni et al., 2003a] B. Apolloni, S. Bassis, A. Brega, S. Gaito, D. Malchiodi and A. M. Zanaboni, A man-machine human interface for a special device of the pervasive computing world, in A. Kameas and N. Streitz (Eds.), Proceedings of DC Tales: Tales of the Disappearing Computer, Santorini Greece, June 1-4, 2003, CTI Press (ISBN 960-406-461-4), 263-267, 2003 [ BIBTEX ]

[Apolloni et al., 2003b] B. Apolloni, A. Brega, D. Malchiodi, N. Valcamonica and A. M. Zanaboni, A symbolic description of the awareness state in car driving, in A. Kameas and N. Streitz (Eds.), Proceedings of DC Tales: Tales of the Disappearing Computer, Santorini Greece, June 1-4, 2003, CTI Press (ISBN 960-406-461-4), 93-96, 2003 [ BIBTEX ]

[Kasderidis et al., 2003] S. Kasderidis, J. G. Taylor, N. Tsapatoulis and D. Malchiodi, Driving Attention to the Dangerous, in O. Kaynak, E. Alpaydin and E. Oja (Eds.), Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings, Springer, Lecture Notes in Computer Science 2714, 909-916, 2003 [ on-line access BIBTEX ]

[Apolloni and Malchiodi, 2002a] B. Apolloni and D. Malchiodi, Narrowing confidence interval witdh of PAC learning risk function by algorithmic inference, in On-line proceedings of the 7th International Symposium on Artificial Intelligence and Mathematics (Fort Lauderdale, USA, January 2-4 2002), 2002 [ on-line access BIBTEX ]

[Apolloni et al., 2002b] B. Apolloni, D. Malchiodi, C. Orovas and A. M. Zanaboni, Fuzzy Methods for Simplifying a Boolean Formula Inferred from Examples, in L. Wang, S. Halgamuge and X. Yao (Eds.), FSDK'02, Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery: Computational Intelligence for the E-Age, November 18-22, 2002, Orchid Country Club, Singapore, Vol. 2, (ISBN 981-04-7520-9), 554-558, 2002, extended version in [Apolloni et al., 2005g] [ BIBTEX ]

[Apolloni et al., 2002c] B. Apolloni, S. Bassis, D. Malchiodi and S. Gaito, Cooperative games in a stochastic environment, in E. Damiani, R. Howlett, L. Jain and N. Ichalkaranje (Eds.), Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies - KES 2002 (Proceedings of KES'2002: Sixth Internatinal Conference on Knowledge-Based Intelligent Information & Engineering Systems, Crema, Italy, September 18-19, 2002, , Vol. 82, Amsterdam: IOS Press/Ohmsha, Frontiers in Artificial Intelligence and Applications (ISBN 1-58603-280-1), 296-300, 2002 [ BIBTEX ]

[Apolloni and Malchiodi, 2001a] B. Apolloni and D. Malchiodi, Twisting statistics with properties, in A. Morazevich, V. Levashenko, E. Zaitseva and N. Ichalkaranje (Eds.), Proceedings of ICINASTe 2001: Internatinal Conference on Information, Networks and System Technlogies (Minsk, Belarus, October 2-4, 2001), Minsk: BSEU (ISBN 985-426-692-3), 48-56, 2001 [ BIBTEX ]

[Apolloni et al., 2000] B. Apolloni, D. Malchiodi, C. Orovas and G. Palmas, From synapses to rules, in Workshop notes of ECAI 2000: European Conference on Artificial Intelligence - Workshop of connectionist-symbolic integration: representation, paradigm and algorithms (Berlin, Germany, 2000), 2000 [ BIBTEX ]

Papers in national conference proceedings

[Malchiodi et al., 2010] D. Malchiodi, M. Re and G. Valentini, Uso di Mathematica per la classificazione di dati di qualità variabile, in Mathematica Italia User Group Meeting - Atti del Convegno 2010, Adalta (ISBN 978-88-96810-00-2), 2010 [ BIBTEX ]

[Bulgheroni and Malchiodi, 2009] M. Bulgheroni and D. Malchiodi, Mathematica per l'introduzione dei rudimenti della programmazione nelle scuole superiori, in Atti del Mathematica Italia User Group Meeting, Adalta, 2009 [ BIBTEX ]

[Malchiodi et al., 2009a] D. Malchiodi, S. Bassis and L. Valerio, svMathematica: implementazione in Mathematica di algoritmi di machine learning basati su vettori di supporto, in Atti del Mathematica Italia User Group Meeting, Adalta, 2009 [ BIBTEX ]

[Malchiodi et al., 2009c] D. Malchiodi, S. Bassis and L. Valerio, Discovering regression data quality through clustering methods, in B. Apolloni, M. Marinaro and S. Bassis (Eds.), New Directions in Neural Networks, 18th Italian Workshop on Neural Networks: WIRN 2008, 22-24 May 2008, Vietri sul Mare, IOS Press, FAIA-KBIES vol. 193 (ISBN 0922-6389), 76-85, 2009 [ BIBTEX ]

[Malchiodi, 2008a] D. Malchiodi, The head fake, ovvero insegnando è concesso imbrogliare, in Atti del Mathematica Italia User Group Meeting, Adalta, 2008 [ BIBTEX ]

[Apolloni et al., 2005b] B. Apolloni, D. Iannizzi, D. Malchiodi and W. Pedrycz, Granular Regression, in B. Apolloni, M. Marinaro, G. Nicosia and R. Tagliaferri (Eds.), Neural Nets. 16th Italian Workshop on Neural Nets, WIRN 2005 and International Workshop on Natural and Artificial Immune Systems, NAIS 2005. Vietri sul Mare, Italy, June 2005, Springer, Lecture Notes in Computer Science 3931 (ISBN 3-540-33183-2), 2005 [ doi> on-line access BIBTEX ]

[Apolloni et al., 2005c] B. Apolloni, A. Clivio, S. Bassis, S. Gaito and D. Malchiodi, An Evolution Hypothesis of Bacterial Populations, in B. Apolloni, M. Marinaro, G. Nicosia and R. Tagliaferri (Eds.), Neural Nets. 16th Italian Workshop on Neural Nets, WIRN 2005 and International Workshop on Natural and Artificial Immune Systems, NAIS 2005. Vietri sul Mare, Italy, June 2005, Springer, Lecture Notes in Computer Science 3931 (ISBN 3-540-33183-2), 214-230, 2005 [ doi> online-access BIBTEX ]

[Apolloni et al., 2005d] B. Apolloni, S. Bassis, S. Gaito, D. Malchiodi and A. Minora, Computing confidence intervals for the risk ofa SVM classifier through algorithmic inference, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Biological and Artificial Intelligence Environments, Springer, 225-234, 2005 [ online-access BIBTEX ]

[Apolloni et al., 2005e] B. Apolloni, S. Bassis, S. Gaito, D. Iannizzi and D. Malchiodi, Learning continuous functions through a new linear regression method, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Biological and Artificial Intelligence Environments, Springer, 235-243, 2005 [ online-access BIBTEX ]

[Apolloni et al., 2003c] B. Apolloni, S. Bassis, A. Brega, S. Gaito, D. Malchiodi, . and A. M. Zanaboni, Monitoring of car drivng awareness from biosignals, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003, Springer, Lecture Notes in Computer Science 2859 (ISBN 3-540-20227-7), 269-277, 2003 [ doi> on-line access BIBTEX ]

[Apolloni et al., 2003d] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Cooperative games in a stochastic environment, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003, Springer, Lecture Notes in Computer Science 2859 (ISBN 3-540-20227-7), 25-34, 2003 [ doi> on-line access BIBTEX ]

[Apolloni et al., 2002d] B. Apolloni, D. Malchiodi, S. Gaito and A. M. Zanaboni, Twisting features with properties, in M. Marinaro and R. Tagliaferri (Eds.), Neural Nets WIRN Vietri-01: Proceedings of the 12th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 17-19 May, 2001, Springer, Perspectives in Neural Computing (ISBN 1-85233-505-X), 301-312, 2002 [ BIBTEX ]

Book chapters

[Apolloni et al., 2005g] B. Apolloni, A. Brega, D. Malchiodi, C. Orovas and A. M. Zanaboni, A Fuzzy Method for Learning Simple Boolean Formulas from Examples, in S. Halgamuge and L. Wang (Eds.), Computational Intelligence for Modelling and Prediction, Chapter 26, Springer, Studies in Computational Intelligence, Vol. 2 (ISBN 3-540-26071-4), 367-382, 2005, extended version of [Apolloni et al., 2002b] [ doi> BIBTEX ]

[Apolloni et al., 2002e] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Statistical bases for learning, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 1, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 5-40, 2002 [ BIBTEX ]

[Apolloni et al., 2002f] B. Apolloni, S. Gaito, D. Iannizzi and D. Malchiodi, Learning regression functions, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 3, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 61-73, 2002 [ BIBTEX ]

[Apolloni et al., 2002g] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Cooperative games in a stochastic environment, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 4, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 75-86, 2002 [ BIBTEX ]

[Apolloni et al., 2002h] B. Apolloni, D. Malchiodi, C. Orovas and A. M. Zanaboni, Fuzzy methods for simplifying a Boolean formula inferred from examples, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 7, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 117-128, 2002 [ BIBTEX ]

[Apolloni et al., 2002i] B. Apolloni, S. Gaito and D. Malchiodi, Learning and checking confidence regions for the hazard function of biomedical data, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 13, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 251-260, 2002 [ BIBTEX ]

Theses

[Malchiodi, 2000] D. Malchiodi, Algorithmic approach to the statistical inference of non-Boolean function classes, Università degli Studi di Milano, 2000, PhD thesis in Computational Mathematics and Operations Research [ BIBTEX ]

[Malchiodi, 1996] D. Malchiodi, Algoritmi di apprendimento per reti neurali non standard, Università degli Studi di Milano, 1996, MSc thesis in Computer Science (in Italian) [ BIBTEX ]

Software

[Malchiodi, 2010a] D. Malchiodi, yaplf: yet another python learning framework, python library, 2010 [ BIBTEX ]

[Malchiodi et al., 2009b] D. Malchiodi, S. Bassis and L. Valerio, svMathematica: a Mathematica package for SV classification and regression, Wolfram Mathematica library, 2009 [ BIBTEX ]

[Malchiodi, 2006a] D. Malchiodi, The Mathematica neosAPI package, Wolfram Mathematica library, 2006 [ BIBTEX ]

[Malchiodi, 2006b] D. Malchiodi, xmlRpc: remotely executing code within Mathematica, Wolfram Mathematica library, 2006 [ BIBTEX ]

[Malchiodi, 2006c] D. Malchiodi, A Mathematica bae64 package, Wolfram Mathematica library, 2006 [ BIBTEX ]