Italian Machine Learning and Data Mining research: The last years

With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research.

[1]  Stefan Kramer,et al.  Stochastic Propositionalization of Non-determinate Background Knowledge , 1998, ILP.

[2]  Marco Gori,et al.  Learning with Box Kernels , 2013, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[4]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[5]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[6]  Marcello Pelillo,et al.  A Game-Theoretic Approach to Hypergraph Clustering , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Floriana Esposito,et al.  Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns , 2012, NFMCP.

[8]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming - Theory and Applications , 2008, Probabilistic Inductive Logic Programming.

[9]  Katsumi Inoue,et al.  ILP turns 20 - Biography and future challenges , 2012, Mach. Learn..

[10]  Anna Monreale,et al.  Evolving networks: Eras and turning points , 2013, Intell. Data Anal..

[11]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[12]  Saso Dzeroski,et al.  PAC-learnability of determinate logic programs , 1992, COLT '92.

[13]  Marcello Sanguineti,et al.  Learning with Boundary Conditions , 2013, Neural Computation.

[14]  Nicola Fanizzi,et al.  Fuzzy Clustering for Semantic Knowledge Bases , 2010, Fundam. Informaticae.

[15]  Francesca A. Lisi,et al.  On Ontologies as Prior Conceptual Knowledge in Inductive Logic Programming , 2009, Knowledge Discovery Enhanced with Semantic and Social Information.

[16]  Achim Rettinger,et al.  Mining the Semantic Web , 2012, Data Mining and Knowledge Discovery.

[17]  Fabrizio Riguzzi,et al.  Experimentation of an expectation maximization algorithm for probabilistic logic programs , 2012, Intelligenza Artificiale.

[18]  Alessandro Sperduti,et al.  Supervised neural networks for the classification of structures , 1997, IEEE Trans. Neural Networks.

[19]  Vítor Santos Costa,et al.  Inductive Logic Programming , 2013, Lecture Notes in Computer Science.

[20]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[21]  Andrea Torsello,et al.  Matching as a non-cooperative game , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Bernd Ludwig,et al.  Context relevance assessment and exploitation in mobile recommender systems , 2012, Personal and Ubiquitous Computing.

[23]  Fabrizio Riguzzi,et al.  Applying the information bottleneck to statistical relational learning , 2011, Machine Learning.

[24]  Stefano Ferilli,et al.  A General Similarity Framework for Horn Clause Logic , 2009, Fundam. Informaticae.

[25]  Alessandro Sperduti,et al.  Mining Structured Data , 2010, IEEE Computational Intelligence Magazine.

[26]  Davide Bacciu,et al.  Compositional Generative Mapping for Tree-Structured Data—Part II: Topographic Projection Model , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Francesco Ricci,et al.  Inferring user utility for query revision recommendation , 2013, SAC '13.

[28]  D. E. Matthews.  Evolution and the Theory of Games , 1977 .

[29]  Pedro M. Domingos,et al.  Discriminative Learning of Sum-Product Networks , 2012, NIPS.

[30]  Luc De Raedt,et al.  First-Order jk-Clausal Theories are PAC-Learnable , 1994, Artif. Intell..

[31]  Luc De Raedt,et al.  Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting , 2006, J. Mach. Learn. Res..

[32]  S. Džeroski,et al.  Relational Data Mining , 2001, Springer Berlin Heidelberg.

[33]  Stefano Ferilli,et al.  Discriminative Structure Learning of Markov Logic Networks , 2008, ILP.

[34]  Michèle Sebag,et al.  Relational Learning as Search in a Critical Region , 2003, J. Mach. Learn. Res..

[35]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[36]  Peter A. Flach,et al.  Propositionalization approaches to relational data mining , 2001 .

[37]  Luc De Raedt,et al.  ILP turns 20 , 2011, Machine Learning.

[38]  Michelangelo Ceci,et al.  Discovering Temporal Bisociations for Linking Concepts over Time , 2011, ECML/PKDD.

[39]  Fabrizio Angiulli.  Condensed Nearest Neighbor Data Domain Description , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Davide Bacciu,et al.  A Generative Multiset Kernel for Structured Data , 2012, ICANN.

[41]  Luc De Raedt,et al.  Phase Transitions and Stochastic Local Search in k-Term DNF Learning , 2002, ECML.

[42]  Michelangelo Ceci,et al.  Discovering Evolution Chains in Dynamic Networks , 2012, NFMCP.

[43]  Michèle Sebag,et al.  A Phase Transition-based Perspective on Multiple Instance Kernels Relational Learning , Multiple Instance Problems , Relational Kernels , 2007 .

[44]  Franca Garzotto,et al.  Investigating the Persuasion Potential of Recommender Systems from a Quality Perspective: An Empirical Study , 2012, TIIS.

[45]  Alessandro Sperduti,et al.  A general framework for adaptive processing of data structures , 1998, IEEE Trans. Neural Networks.

[46]  Marcello Pelillo,et al.  Dominant Sets and Pairwise Clustering , 2007 .

[47]  Lorenza Saitta,et al.  Automated Concept Acquisition in Noisy Environments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  Michelangelo Ceci,et al.  A relational approach to probabilistic classification in a transductive setting , 2009, Eng. Appl. Artif. Intell..

[49]  Clara Pizzuti,et al.  Outlier mining in large high-dimensional data sets , 2005, IEEE Transactions on Knowledge and Data Engineering.

[50]  Roser Morante,et al.  Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection , 2011, ILP.

[51]  Roberto Turrin,et al.  Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.

[52]  William W. Cohen.  Pac-learning Recursive Logic Programs: Negative Results , 1994, J. Artif. Intell. Res..

[53]  Michèle Sebag,et al.  Phase Transitions within Grammatical Inference , 2005, IJCAI.

[54]  Fabrizio Riguzzi,et al.  Learning the Structure of Probabilistic Logic Programs , 2011, ILP.

[55]  Clara Pizzuti,et al.  Distance-based detection and prediction of outliers , 2006, IEEE Transactions on Knowledge and Data Engineering.

[56]  Alessio Micheli,et al.  Neural Network for Graphs: A Contextual Constructive Approach , 2009, IEEE Transactions on Neural Networks.

[57]  Marco Gori,et al.  Constraint Verification With Kernel Machines , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[58]  Michèle Sebag,et al.  Tractable Induction and Classification in First Order Logic Via Stochastic Matching , 1997, IJCAI.

[59]  Peter Kontschieder,et al.  Evolutionary Hough Games for coherent object detection , 2012, Comput. Vis. Image Underst..

[60]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[61]  Alessandro Sperduti,et al.  Special issue on neural networks and kernel methods for structured domains , 2005, Neural Networks.

[62]  Donato Malerba,et al.  Flexible Matching for Noisy Structural Descriptions , 1991, IJCAI.

[63]  Alfredo Petrosino,et al.  Encoding nondeterministic fuzzy tree automata into recursive neural networks , 2004, IEEE Transactions on Neural Networks.

[64]  Francesc Esteva,et al.  Review of Triangular norms by E. P. Klement, R. Mesiar and E. Pap. Kluwer Academic Publishers , 2003 .

[65]  Alessio Micheli,et al.  Application of Cascade Correlation Networks for Structures to Chemistry , 2004, Applied Intelligence.

[66]  Francesco Ricci,et al.  Context-based splitting of item ratings in collaborative filtering , 2009, RecSys '09.

[67]  Andrea Torsello,et al.  Imposing Semi-Local Geometric Constraints for Accurate Correspondences Selection in Structure from Motion: A Game-Theoretic Perspective , 2011, International Journal of Computer Vision.

[69]  Bernd Ludwig,et al.  Matrix factorization techniques for context aware recommendation , 2011, RecSys '11.

[70]  Alessio Micheli,et al.  Contextual processing of structured data by recursive cascade correlation , 2004, IEEE Transactions on Neural Networks.

[71]  Francesco Ricci,et al.  Active learning strategies for rating elicitation in collaborative filtering , 2013, ACM Trans. Intell. Syst. Technol..

[72]  Céline Rouveirol,et al.  Lazy Propositionalisation for Relational Learning , 2000, ECAI.

[73]  Dino Pedreschi,et al.  Unveiling the complexity of human mobility by querying and mining massive trajectory data , 2011, The VLDB Journal.

[74]  Jean-Gabriel Ganascia,et al.  Representation Changes for Efficient Learning in Structural Domains , 1996, ICML.

[75]  Luigi Palopoli,et al.  Discovering Characterizations of the Behavior of Anomalous Subpopulations , 2013, IEEE Transactions on Knowledge and Data Engineering.

[76]  Nicola Fanizzi,et al.  Ideal Theory Refinement under Object Identity , 2000, ICML.

[77]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[78]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[79]  Luigi Palopoli,et al.  Detecting outlying properties of exceptional objects , 2009, TODS.

[80]  Lorenza Saitta,et al.  Phase Transitions in Relational Learning , 2000, Machine Learning.

[81]  Kwang-Ho Ro,et al.  Outlier detection for high-dimensional data , 2015 .

[82]  Marcello Pelillo,et al.  Similarity-Based Pattern Analysis and Recognition , 2013, Advances in Computer Vision and Pattern Recognition.

[83]  Fabrizio Angiulli,et al.  Prototype-Based Domain Description for One-Class Classification , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[84]  Donato Malerba,et al.  Using trend clusters for spatiotemporal interpolation of missing data in a sensor network , 2013, J. Spatial Inf. Sci..

[85]  Fabrizio Angiulli,et al.  DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets , 2009, TKDD.

[86]  Aomar Osmani,et al.  Empirical Study of Relational Learning Algorithms in the Phase Transition Framework , 2009, ECML/PKDD.

[87]  Luc De Raedt,et al.  kFOIL: Learning Simple Relational Kernels , 2006, AAAI.

[88]  Céline Rouveirol,et al.  Towards Learning in CARIN-ALN , 2000, ILP.

[89]  Pasquale Lops,et al.  Combining Learning and Word Sense Disambiguation for Intelligent User Profiling , 2007, IJCAI.

[90]  Ni Lao,et al.  Efficient Relational Learning with Hidden Variable Detection , 2010, NIPS.

[91]  Francesco Ricci,et al.  Location-aware music recommendation , 2013, International Journal of Multimedia Information Retrieval.

[92]  John F. Kolen,et al.  From Sequences to Data Structures: Theory and Applications , 2001 .

[93]  Davide Bacciu,et al.  Compositional Generative Mapping for Tree-Structured Data—Part I: Bottom-Up Probabilistic Modeling of Trees , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[94]  Barbara Hammer,et al.  Learning with recurrent neural networks , 2000 .

[95]  Pasquale Lops,et al.  Cross-Language Information Filtering: Word Sense Disambiguation vs. Distributional Models , 2011, AI*IA.

[96]  Nir Friedman,et al.  Probabilistic Graphical Models , 2009, Data-Driven Computational Neuroscience.

[97]  Pasquale Lops,et al.  Knowledge infusion into content-based recommender systems , 2009, RecSys '09.

[98]  Bernd Ludwig,et al.  InCarMusic: Context-Aware Music Recommendations in a Car , 2011, EC-Web.

[99]  Stefano Ferilli,et al.  Stochastic Propositionalization for Efficient Multi-relational Learning , 2008, ISMIS.

[100]  Fabrizio Riguzzi,et al.  Expectation maximization over binary decision diagrams for probabilistic logic programs , 2013, Intell. Data Anal..

[101]  Francesco Ricci,et al.  Optimal radio channel recommendations with explicit and implicit feedback , 2012, RecSys.

[102]  Marco Gori,et al.  Unsupervised Learning by Minimal Entropy Encoding , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[103]  Aykut Erdem,et al.  Graph Transduction as a Noncooperative Game , 2012, Neural Computation.

[104]  Katharina Morik,et al.  A Polynomial Approach to the Constructive Induction of Structural Knowledge , 2004, Machine Learning.

[105]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD 2000.

[106]  Michelangelo Ceci,et al.  A Temporal Data Mining Framework for Analyzing Longitudinal Data , 2011, DEXA.

[107]  Franco Scarselli,et al.  Recursive processing of cyclic graphs , 2006, IEEE Transactions on Neural Networks.

[108]  Daphna Weinshall,et al.  Classification with Nonmetric Distances: Image Retrieval and Class Representation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[109]  Anna Monreale,et al.  WhereNext: a location predictor on trajectory pattern mining , 2009, KDD.

[110]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[111]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[112]  Michelangelo Ceci,et al.  Transductive learning for spatial regression with co-training , 2010, SAC '10.

[113]  Alexander M. Bronstein,et al.  A game-theoretic approach to deformable shape matching , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[114]  Marco Gori,et al.  Bridging logic and kernel machines , 2011, Machine Learning.

[115]  Alessio Micheli,et al.  Universal Approximation Capability of Cascade Correlation for Structures , 2005, Neural Computation.

[116]  Stefano Ferilli,et al.  Social networks and statistical relational learning: a survey , 2012, Int. J. Soc. Netw. Min..

[117]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[118]  Raymond T. Ng,et al.  A Unified Notion of Outliers: Properties and Computation , 1997, KDD.

[119]  Nicola Fanizzi,et al.  Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge , 2012, SAC '12.

[120]  Michelangelo Ceci,et al.  Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach , 2004, PKDD.

[121]  Jacques Demongeot,et al.  Boundary conditions and phase transitions in neural networks. Theoretical results , 2008, Neural Networks.

[122]  Donato Malerba,et al.  Summarization for Geographically Distributed Data Streams , 2010, KES.

[123]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[124]  Franca Garzotto,et al.  User profiling vs. accuracy in recommender system user experience , 2012, AVI.

[125]  Stefano Ferilli,et al.  Relational Temporal Data Mining for Wireless Sensor Networks , 2009, AI*IA.

[126]  Aomar Osmani,et al.  On the connection between the phase transition of the covering test and the learning success rate in ILP , 2008, Machine Learning.

[127]  Volker Tresp,et al.  Mining the Semantic Web Statistical Learning for Next Generation Knowledge Bases , 2012 .

[128]  Michelangelo Ceci,et al.  Mr-SBC: A Multi-relational Naïve Bayes Classifier , 2003, PKDD.

[129]  Claudio Gallicchio,et al.  Tree Echo State Networks , 2013, Neurocomputing.

[130]  Michelangelo Ceci,et al.  Relational Learning: Statistical Approach Versus Logical Approach in Document Image Understanding , 2005, AI*IA.

[131]  Francesco Ricci,et al.  Context-Aware Recommender Systems , 2011, AI Mag..

[132]  Michèle Sebag,et al.  C4.5 competence map: a phase transition-inspired approach , 2004, ICML '04.

[133]  Robert P. W. Duin,et al.  The Dissimilarity Representation for Pattern Recognition - Foundations and Applications , 2005, Series in Machine Perception and Artificial Intelligence.

[134]  Alessio Micheli,et al.  Recursive self-organizing network models , 2004, Neural Networks.

[135]  Magnus Sahlgren,et al.  The Word-Space Model: using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces , 2006 .

[136]  Christian Eitzinger,et al.  Triangular Norms , 2001, Künstliche Intell..

[137]  Michelangelo Ceci,et al.  Transductive Learning for Spatial Data Classification , 2010, Advances in Machine Learning I.

[138]  Jens Lehmann,et al.  Concept learning in description logics using refinement operators , 2009, Machine Learning.

[139]  Stefano Ferilli,et al.  Boosting learning and inference in Markov logic through metaheuristics , 2011, Applied Intelligence.

[140]  Michelangelo Ceci,et al.  Dealing with spatial autocorrelation when learning predictive clustering trees , 2013, Ecol. Informatics.

[141]  Ji Zhang,et al.  Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance , 2006, Knowledge and Information Systems.

[142]  Donato Malerba,et al.  A relational perspective on spatial data mining , 2008, Int. J. Data Min. Model. Manag..

[143]  Luc De Raedt,et al.  Fast learning of relational kernels , 2010, Machine Learning.

[144]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[145]  Yujie Zhang,et al.  Context-Aware Recommender Systems: Context-Aware Recommender Systems , 2012 .

[146]  Francesco Ricci,et al.  Group recommendations with rank aggregation and collaborative filtering , 2010, RecSys '10.