Team DREAM

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Dissemination
Bibliography

Bibliography

Major publications by the team in recent years

[1]
P. Besnard, M.-O. Cordier, Y. Moinard.
Ontology-based inference for causal explanation, in: Integrated Computer-Aided Engineering, 2008, vol. 15, no 4, p. 351-367.
[2]
G. Carrault, Marie-Odile. Cordier, R. Quiniou, F. Wang.
Temporal abstraction and Inductive Logic Programming for arrhyhtmia recognition from electrocardiograms, in: Artificial Intelligence in Medicine, 2003, vol. 28, p. 231-263.
[3]
M.-O. Cordier, F. Garcia, C. Gascuel-Odoux, V. Masson, J. Salmon-Monviola, F. Tortrat, R. Trépos.
A machine learning approach for evaluating the impact of land use and management practices on streamwater pollution by pesticides, in: MODSIM'05 (International Congress on Modelling and Simulation), Modelling and Simulation Society of Australia and New Zealand (editor), December 2005.
[4]
Y. Pencolé, M.-O. Cordier.
A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks, in: Artificial Intelligence Journal, 2005, vol. 164, no 1-2, p. 121-170.

Publications of the year

Articles in International Peer-Reviewed Journal

[5]
P. Aurousseau, H. Squividant, R. Trépos, F. Tortrat, C. Gascuel-Odoux, M.-O. Cordier.
A plot drainage network as a conceptual tool for the spatialisation of surface flow pathways for agricultural catchments, in: Computer and Geosciences, 2009, vol. 35, p. 276-288
http://www.sciencedirect.com/science/article/B6V7D-4TXF7YP-2/2/7d57a4059309e57c3230cf52ef4928d4.
[6]
E. Fromont, R. Quiniou, M.-O. Cordier.
Learning Rules from Multisource Data for Cardiac Monitoring, in: International Journal of Biomedical Engineering and Technology (IJBET), 2009, vol. 2, no 3, to appear.
[7]
C. Gascuel-Odoux, P. Aurousseau, M.-O. Cordier, P. Durand, F. Garcia, V. Masson, J. Salmon-Monviola, F. Tortrat, R. Trépos.
A decision-oriented model to evaluate the effect of land use and agricultural management on herbicide contamination in stream water, in: Environmental Modelling and Software, 2010, p. 1-14, to appear.
[8]
Y. Yan, P. Dague, Y. Pencolé, M.-O. Cordier.
A Model-based Approach for Diagnosing Faults in Web Service Processes, in: International Journal of Web Services Research (IJWSR), 2009, vol. 6, no 1, p. 87–110.

International Peer-Reviewed Conference/Proceedings

[9]
M.-O. Cordier, X. Le Guillou, S. Robin, L. Rozé.
Monitoring WS-CDL-based choreographies of Web Services, in: DX'09 (20th International Worshop On Principles of Diagnosis), Stockholm, Sweden, june 2009.
[10]
C. Largouet, M.-O. Cordier, G. Fontenelle.
Scenario templates to analyse qualitative ecosystem models, in: 18th World IMACS Congress and MODSIM09 (International Congress on Modelling and Simulation. Modelling and Simulation), R.S. Anderssen, R.D. Braddock, L.T.H. Newham (editors), 2009, p. 2129-2135
http://mssanz.org.au/modsim09, ISBN: 978-0-9758400-7-8.
[11]
W. Wang, T. Guyet, S. Knapskog.
Autonomic Intrusion Detection System, in: Symposium on recent advanced in intrusion detection (RAID), 2009, p. 359–361
http://dx.doi.org/10.1007/978-3-642-04342-0_24.
[12]
W. Wang, F. Masseglia, T. Guyet, R. Quiniou, M.-O. Cordier.
A General Framework for Adaptive and Online Detection of Web attacks, in: international world wide web conference (WWW 2009), 2009, p. 1141-1142
http://doi.acm.org/10.1145/1526709.1526897.

National Peer-Reviewed Conference/Proceedings

[13]
T. Guyet, R. Quiniou, M.-O. Cordier, W. Wang.
Diagnostic multi-sources adaptatif Application à la détection d'intrusion dans des serveurs Web, in: EGC 2009, january 2009.
[14]
C. Largouet, M.-O. Cordier.
Patrons de scénarios pour l'exploration qualitative d'un écosystème, in: RFIA 2010 (Reconnaissance des Formes et Intelligence Artificielle), 2010
http://rfia2010.info.unicaen.fr/, to appear.
[15]
X. Le Guillou, M.-O. Cordier, S. Robin, L. Rozé.
Surveillance de choregraphies de Web Services basees sur WS-CDL, in: RJCIA'09 (9e Rencontres des Jeunes Chercheurs en Intelligence Artificielle), Hammamet, Tunisie, mai 2009.
[16]
W. Wang, T. Guyet, R. Quiniou, M.-O. Cordier, F. Masseglia, B. Trousse.
Online and Adaptive anomaly Detection: detecting intrusions in unlabelled audit data streams, in: EGC 2009, 2009.

Workshops without Proceedings

[17]
M.-O. Cordier, T. Guyet, C. Largouet, V. Masson, H.-M. Suchier.
Apprentissage incrémental de règles de décision à partir de données d'un simulateur, in: Atelier INFORSID : Systèmes d'Information et de Décision pour l'Environnement, 2009
http://www.irisa.fr/dream/Pages_Pros/Thomas.Guyet/publis/SIDE2009_Cordier.pdf.
[18]
M.-O. Cordier, Y. Moinard.
Remarks about the use of Answer Set Programming for a causal formalism, in: MICRAC project Workshop (Toulouse), jun 2009
http://www.irit.fr/MICRAC/colloque/index_C.html.
[19]
T. Guyet, R. Quiniou, M.-O. Cordier.
LogAnalyzer : monitoring adaptatif d'un flux de données, in: RFIA 2010 (Congrès Reconnaissance des Formes et Intelligence Artificielle), 2010, Demonstration, to appear.

Scientific Books (or Scientific Book chapters)

[20]
M.-O. Cordier, M. Falchier, F. Garcia, C. Gascuel-Odoux, D. Heddadj, L. Lebouille, V. Masson, J. Salmon-Monviola, F. Tortrat, R. Trépos.
Chapitre 9 : Modélisation d'un transfert d'herbicides dans un bassin versant dans le cadre d'un outil d'aide à la décision pour la maîtrise de la qualité des eaux, in: Construire la décision : démarches en agriculture, agro-alimentaire et espace rural, Quae, 2009, p. 41-55.
[21]
R. Quiniou, L. Callens, G. Carrault, M.-O. Cordier, E. Fromont, P. Mabo, F. Portet.
Intelligent adaptive monitoring for cardiac surveillance, in: Computational Intelligence in Healthcare, L. C. Jain (editor), LNAI, Springer Verlag, 2009, to appear.
[22]
The WS-Diamond Team.
Chapter 2: WS-DIAMOND: Web Services   DIAgnosability, MONitoring and Diagnosis, in: At your service: An overview of results of projects in the field of service engineering of the IST programme, J. Mylopoulos, M. Papazoglou (editors), Series on Information Systems, MIT Press Series on Information Systems, 2009.

Internal Reports

[23]
T. Guyet, R. Quiniou, W. Wang, M.-O. Cordier.
Self-adaptive web intrusion detection system, INRIA, 2009, no 6989
http://hal.inria.fr/inria-00406450/en/, Technical report.

References in notes

[24]
B. Dubuisson (editor)
Diagnostic, intelligence artificielle et reconnaissance des formes, Traité IC2 : Information - Commande - Communication, Hermes, 2001.
[25]
S. Dzeroski, N. Lavrač (editors)
Relational Data Mining, Springer, Berlin, 2001.
[26]
W. Hamscher, L. Console, J. de Kleer (editors)
Readings in Model-Based Diagnosis, Morgan Kaufmann, San Meteo, CA, Etats-Unis, 1992.
[27]
C. Aggarwal.
Data Streams: Models and Algorithms, Advances in Database Systems, Springer, 2007.
[28]
A. Aghasaryan, E. Fabre, A. Benveniste, R. Boubour, C. Jard.
Fault detection and diagnosis in distributed systems : an approach by partially stochastic Petri nets, in: Discrete Event Dynamic Systems, Juin 1998, vol. 8, no 2, p. 203-231, Special issue on Hybrid Systems.
[29]
R. Agrawal, T. Imielinski, A. N. Swami.
Mining Association Rules between Sets of Items in Large Databases, in: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., P. Buneman, S. Jajodia (editors), 26–28  1993, p. 207–216.
[30]
R. Agrawal, R. Srikant.
Mining sequential patterns, in: Eleventh International Conference on Data Engineering, Taipei, Taiwan, P. S. Yu, A. S. P. Chen (editors), IEEE Computer Society Press, 1995, p. 3–14.
[31]
C. Baral.
Knowledge representation, reasoning and declarative problem solving, Cambridge University Press, 2003, 2003.
[32]
P. Baroni, G. Lamperti, P. Pogliano, M. Zanella.
Diagnosis of large active systems, in: Artificial Intelligence, 1999, vol. 110, p. 135-183.
[33]
M. Basseville, M.-O. Cordier.
Surveillance et diagnostic de systèmes dynamiques : approches complémentaires du traitement de signal et de l'intelligence artificielle, Irisa, 1996, no 1004
http://www.irisa.fr/centredoc/publis/PI/1996/irisapublication.2006-02-14.3475788484, Technical report.
[34]
Philippe. Besnard, M.-O. Cordier.
Inferring causal explanations, in: Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU'99), A. Hunter, S. Parsons (editors), Lecture Notes in Artificial Intelligence, Springer-Verlag, 1999, vol. 1638, p. 55-67.
[35]
P. Besnard, M.-O. Cordier, Y. Moinard.
Configurations for Inference between Causal Statements, in: KSEM 2006 (First Int. Conf. on Knowledge Science, Engineering and Management), J. Lang, F. Lin, J. Wang (editors), LNAI, Springer, aug 2006, no 4092, p. 292–304
http://www.irisa.fr/dream/dataFiles/moinard/causeksempub.pdf.
[36]
P. Besnard, M.-O. Cordier, Y. Moinard.
Ontology-based inference for causal explanation, in: KSEM07 (Second International Conference on Knowledge Science, Engineering and Management), Z. Zhang, J. Siekmann (editors), LNAI, Springer, nov 2007, no 4798, p. 153-164
http://www.irisa.fr/dream/dataFiles/moinard/ksem07bcausesonto.pdf.
[37]
P. Besnard, M.-O. Cordier, Y. Moinard.
Deriving explanations from causal information, in: ECAI 2008 (18th European Conference on Artificial Intelligence), Patras, Greece, M. Ghallab, C. D. Spytopoulos, N. Fakotakis, N. Avouris (editors), IOS Press, jul 2008, p. 723–724.
[38]
P. Besnard, M.-O. Cordier, Y. Moinard.
Ontology-based inference for causal explanation, in: Integrated Computer-Aided Engineering, 2008, vol. 15, no 4, p. 351-367.
[39]
C. Biernacki, G. Celeux, G. Govaert, F. Langrognet.
Model-Based Cluster and Discriminant Analysis with the MIXMOD Software, in: Computational Statistics and Data Analysis, 2006, vol. 51, no 2, p. 587-600.
[40]
A. Bochman.
A Causal Theory of Abduction, in: 7th Int. Symposium on Logical Formalizations of Common Sense Reasoning, S. McIlraith, P. Peppas, M. Thielscher (editors), 2005, p. 33–38.
[41]
F. Calimeri, S. Cozza, G. Iann, N. Leone.
An ASP System with Functions, Lists, and Sets, in: 10th International Conference on Logic Programming and Non- monotonic Reasoning (LPNMR'09), E. Erdem, F. Lin, T. Schaub (editors), LNCS, Springer Verlag, 2009, vol. 5753, p. 483-489.
[42]
F. Calimeri, G. Ianni.
Template Programs for Disjunctive Logic Programming: An Operational Semantics, in: AI Communications, 2006, vol. 19, p. 193-206.
[43]
Y. Chi, S. Nijssen, R. R. Muntz, J. N. Kok.
Frequent Subtree Mining–An Overview, in: Fundamenta Informaticae, IOS Press, 2005, vol. 66, p. 161–198.
[44]
M.-O. Cordier, Y. Pencolé, L. Travé-Massuyès, T. Vidal.
Self-healablity = diagnosability + repairability, in: DX07 (18th International Workshop on Principles of Diagnosis), Nashville, TN, USA, May 2007, p. 251–258.
[45]
M.-O. Cordier, Y. Pencolé, L. Travé-Massuyès, T. Vidal.
Characterizing and checking self-healibility, in: ECAI 2008 (18th European Conference on Artificial Intelligence), Patras, Greece, M. Ghallab, C. D. Spytopoulos, N. Fakotakis, N. Avouris (editors), IOS Press, 2008, p. 789–790.
[46]
M.-O. Cordier, L. Travé-Massuyès, X. Pucel.
Comparing diagnosability in continuous and discrete-events systems, in: Safeprocess'2006, Beijing, China, 2006
http://www.irisa.fr/dream/dataFiles/cordier/safe06-cordetal.pdf.
[47]
A. Cornuéjols, L. Miclet.
Apprentissage artificiel : concepts et algorithmes, Eyrolles, 2002.
[48]
R. Debouk, S. Lafortune, D. Teneketzis.
Coordinated Decentralized Protocols for Failure Diagnosis of Discrete Event Systems, in: Discrete Event Dynamic Systems, 2000, vol. 10, no 1-2, p. 33-86.
[49]
J. de Kleer, A. Mackworth, R. Reiter.
Characterizing diagnoses and systems, in: Artificial Intelligence, 1992, vol. 56, no 2-3, p. 197-222.
[50]
J. de Kleer, B. C. Williams.
Diagnosis with behavioral modes, in: Proceedings of the 11th International Joint Conference on Artificial Intelligence IJCAI'89, Detroit, MI, Etats-Unis, 1989, p. 1324-1330.
[51]
L. De Raedt.
A perspective on inductive databases, in: SIGKDD Explor. Newsl., 2002, vol. 4, no 2, p. 69–77
http://doi.acm.org/10.1145/772862.772871.
[52]
C. Dousson.
Chronicle Recognition System, 1994.
[53]
C. Dousson.
Suivi d'évolutions et reconnaissance de chroniques, Université Paul Sabatier de Toulouse, LAAS-CNRS, Toulouse, 1994, Ph. D. Thesis.
[54]
C. Dousson, T. V. Duong.
Discovering Chronicles with Numerical Time Constraints from Alarm Logs for Monitoring Dynamic Systems., in: IJCAI, T. Dean (editor), Morgan Kaufmann, 1999, p. 620-626.
[55]
C. Dousson, P. Gaborit, M. Ghallab.
Situation recognition: representation and algorithms, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Chambéry, France, 1993, p. 166-172.
[56]
C. Drescher, M. Gebser, T. Grote, B. Kaufmann, A. König, M. Ostrowski, T. Schaub.
Conflict-driven disjunctive answer set solving, in: Eleventh International Conference on Principles of Knowledge Representation and Reasoning (KR'08), E. Erdem, F. Lin, T. Schaub (editors), AAAI Press, 2008, p. 422–432.
[57]
S. Dzeroski, L. Todorovski.
Discovering dynamics: from inductive logic programming to machine discovery, in: Journal of Intelligent Information Systems, 1995, vol. 4, p. 89-108.
[58]
E. Fabre, A. Benveniste, C. Jard, L. Ricker, M. Smith.
Distributed State Reconstruction for Discrete Event Systems., in: Proc. of the 2000 IEEE Control and Decision Conference (CDC), Sydney, Australie, 2000.
[59]
B. Frey, D. Dueck.
Clustering by passing messages between data points, in: Science, 2007, vol. 315, p. 972–976.
[60]
J. Gao, B. Ding, W. Fan, J. Han, P. S. Yu.
Classifying Data Streams with Skewed Class Distributions and Concept Drifts, in: IEEE Internet Computing, 2008, vol. 12, no 6, p. 37-49.
[61]
M. Garofalakis, J. Gehrke, R. Rastogi.
Querying and Mining Data Streams: You Only Get One Look. Tutorial notes, in: ACM Int. Conf. on Management of Data, 2002.
[62]
E. Giunchiglia, J. Lee, V. Lifschitz, N. McCain, H. Turner.
Nonmonotonic causal theories, in: Artificial Intelligence Journal, March 2004, vol. 153, no 1–2, p. 49–104.
[63]
A. Grastien, M.-O. Cordier, C. Largouët.
Automata Slicing for Diagnosing Discrete-Event Systems with Partially Ordered Observations, in: AIIA'05 (Congress of the Italian Association for Artificial Intelligence), Septembre 2005, Milan, Italy p
http://hal.inria.fr/inria-00000531.
[64]
GROUPE ALARME.
Monitoring and alarm interpretation in industrial environments, in: AI Communications, 1998, vol. 11, 3-4, p. 139-173, S. Cauvin, Marie-Odile Cordier, Christophe Dousson, P. Laborie, F. Lévy, J. Montmain, M. Porcheron, I. Servet, L. Travé-Massuyès.
[65]
T. Guyet, R. Quiniou.
Mining temporal patterns with quantitative intervals, in: 4th International Workshop on Mining Complex Data at ICDM 2008, December 2008.
[66]
D. T. Hau, E. W. Coiera.
Learning qualitative models of dynamic systems, in: Machine Learning, 1997, vol. 26, p. 177-211.
[67]
T. Imielinski, H. Mannila.
A database perspective on knowledge discovery, in: Comm. of The ACM, 1996, vol. 39, p. 58–64
http://citeseer.ist.psu.edu/imielinski96database.html.
[68]
T. Jéron, H. Marchand, S. Pinchinat, M.-O. Cordier.
Supervision patterns in discrete event systems diagnosis, in: WODES'06 (8th International Workshop on Discrete Event Systems), Ann Arbor, Michigan, USA, IEEE (ISBN 1-4244-0053-8), 2006
http://www.irisa.fr/vertecs/Publis/Ps/PI-1784.pdf, (extended version in Irisa Technical Report 1784).
[69]
M. Kubat, J. Gama, P. E. Utgoff.
Incremental learning and concept drift, in: Intell. Data Anal., 2004, vol. 8, no 3.
[70]
S. D. Lee, L. De Raedt.
Constraint Based Mining of First Order Sequences in SeqLog, in: Database Support for Data Mining Applications, LNCS, Springer-Verlag, 2003, vol. 2682.
[71]
X. Le Guillou, M.-O. Cordier, S. Robin, L. Rozé.
Chronicles for On-line Diagnosis of Distributed Systems, IRISA, Rennes, France, may 2008, no 1890, Technical report.
[72]
X. Le Guillou, M.-O. Cordier, S. Robin, L. Rozé.
Chronicles for On-line Diagnosis of Distributed Systems, in: ECAI'08 (18th European Conference on Artificial Intelligence), Patras, Greece, M. Ghallab, C. D. Spytopoulos, N. Fakotakis, N. Avouris (editors), IOS Press, july 2008.
[73]
X. Le Guillou.
Chronicles for On-line Diagnosis of Distributed Systems, in: CAISE-dc'08 (Conference on Advanced Information Systems Engineering - doctoral consortium), Montpellier, France, june 2008.
[74]
N. Leone, G. Pfeifer, W. Faber, T. Eiter, G. Gottlob, S. Perri, F. Scarcello.
The DLV System for Knowledge Representation and Reasoning, in: ACM Transactions on Computational Logic (TOCL), 2006, vol. 7, no 3, p. 499–562.
[75]
H. Mannila, H. Toivonen, A. I. Verkamo.
Discovery of Frequent Episodes in Event Sequences, in: Data Mining and Knowledge Discovery, 1997, vol. 1, no 3, p. 259–289.
[76]
A. Marascu.
Extraction de motifs séquentiels dans les flux de données, University of Nice-Sophia Antipolis, september 2009, Ph. D. Thesis.
[77]
A. Marascu, F. Masseglia.
Mining sequential patterns from data streams: a centroid approach, in: J. Intell. Inf. Syst., 2006, vol. 27, no 3, p. 291–307
http://dx.doi.org/10.1007/s10844-006-9954-6.
[78]
D. Page.
ILP: Just Do It, in: Proceedings of ILP'2000, J. Cussens, A. Frisch (editors), LNAI, Springer, 2000, vol. 1866, p. 3-18.
[79]
Y. Pencolé, M.-O. Cordier, L. Rozé.
Incremental decentralized diagnosis approach for the supervision of a telecommunication network, in: DX'01 (International Workshop on Principles of Diagnosis), Sansicario, Italy, 2001
http://www.irisa.fr/dream/dataFiles/ypencole/DX01.pdf.
[80]
K. B. Pratt, E. Fink.
Search for Patterns in Compressed Time Series, in: International Journal of Image and Graphics, 2002, vol. 2, no 1, p. 89–106.
[81]
René. Quiniou, M.-O. Cordier, Guy. Carrault, F. Wang.
Application of ILP to cardiac arrhythmia characterization for chronicle recognition, in: ILP'2001, C. Rouveirol, M. Sebag (editors), LNAI, Springer-Verlag, 2001, vol. 2157, p. 220-227
http://www.irisa.fr/dream/dataFiles/quiniou/ilp01.pdf.
[82]
N. Ramaux, M. Dojat, D. Fontaine.
Temporal scenario recognition for intelligent patient monitoring, in: Proc. of the 6th Conference on Artificial Intelligence in Medecine Europe (AIME'97), 1997.
[83]
R. Reiter.
A theory of diagnosis from first principles, in: Artificial Intelligence, 1987, vol. 32, no 1, p. 57-96.
[84]
L. Rozé, M.-O. Cordier.
Diagnosing discrete-event systems : extending the “diagnoser approach” to deal with telecommunication networks, in: Journal on Discrete-Event Dynamic Systems : Theory and Applications (JDEDS), 2002, vol. 12, no 1, p. 43-81
http://www.irisa.fr/dream/dataFiles/cordier/jeds.pdf.
[85]
L. Rozé, M.-O. Cordier.
Diagnosing Discrete Event Sytems : An experiment in Telecommunication Networks, in: WODES98, Fourth Workshop on Discrete Event Systems, Cagliari, Italy, 1998, p. 130-137.
[86]
Laurence. Rozé.
Supervision de réseaux de télécommunication : une approche à base de modèles, Université de Rennes 1, 1997, Ph. D. Thesis.
[87]
M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, D. Teneketzis.
Diagnosability of discrete event systems, in: Proceedings of the International Conference on Analysis and Optimization of Systems, 1995, vol. 40, p. 1555-1575.
[88]
M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, D. Teneketzis.
Active Diagnosis of Discrete-Event Systems, in: IEEE Transactions on Automatic Control, 1998, vol. 43, no 7, p. 908-929.
[89]
M. Sebag, C. Rouveirol.
Constraint Inductive Logic Programming, in: Advances in Inductive Logic Programming, L. De Raedt (editor), IOS Press, 1996, p. 277-294.
[90]
G. Shafer.
A Mathematical Theory of Evidence, Princeton University Press, 1976.
[91]
R. Trépos, A. Salleb, M.-O. Cordier, V. Masson, C. Gascuel-Odoux.
A Distance Based Approach for Action Recommendation, in: ECML 05 (European Conference on Machine Learning), Porto, Portugal, Lecture Notes in Artificial Intelligence, Springer, october 2005.
[92]
R. Trépos, V. Masson, M.-O. Cordier, C. Gascuel-Odoux.
Induction de motifs spatiaux décrivant les chemins de ruissellement, in: Atelier Représentation et Raisonnement sur le Temps et l'Espace (RTE'08), Montpellier, France, Juin 2008, p. 1-13.
[93]
R. Trépos.
Apprentissage symbolique à partir de données issues de simulation- Gestion d'un bassin versant pour une meilleure qualité de l'eau, Université de Rennes 1, january 2008, Ph. D. Thesis.
[94]
A. Vautier, M.-O. Cordier, R. Quiniou.
An Inductive Database for Mining Temporal Patterns in Event Sequences (short version), in: Proceedings of IJCAI-05 (International Joint Conference on Artificial Intelligence), Edinburgh, L. P. Kaelbling, A. Saffiotti (editors), 2005, p. 1640-1641, Poster.
[95]
G. Widmer, M. Kubat.
Learning in the Presence of Concept Drift and Hidden Contexts, in: Machine Learning, 1996, vol. 23, no 1, p. 69-101.

previous
next