|
Nikolaev,N. and Smirnov, E. (2009). Unscented
Grid Filtering and Smoothing for Nonlinear Time Series Analysis, In:
Proc. of the Int. Conference on Neural Networks ICNN-2009, Oslo,
Norway, pp.222-228.
M. Moed, Evgueni N. Smirnov (2009). Efficient
AdaBoost Region Classification. In Procceding of 6th International
Conference Machine Learning and Data Mining in Pattern Recognition (MLDM
2009), Leipzig, Germany, July 23-25, 2009. Lecture Notes in Computer
Science 5632 Springer, pp.: 123-136.
Smirnov, E.N., Nikolaev, N.,
and Nalbantov, G.I.. (2008). Description Identification and the
Consistency Problem. In
Proceedings of the 28th SGAI International Conference on Innovative
techniques and Applications of Artificial Intelligence (AI 2004),
Springer, December, 2008, Cambridge, UK, 2008.
Van Prehn, J., and Smirnov,
E.N., (2008). Region Classification with Decision Trees. In:
Proceedings of the Second
IEEE International Workshop on Reliability Issues in Knowledge
Discovery (RIKD-08), IEEE Computer Society, pp. 53-59,
Pisa, Italy, December 15, 2008.
Smirnov, E.N., Winands,M., Spronck, P., and Schadd, M.
(2007).
Constructing Reliable Classifiers for Road Side
Assistance. Proceedings of the
Annual Belgian-Dutch Machine Learning Conference (Benelearn 2007)
(eds. van Someren, M., Katrenko, S., and Adriaans, P.),
pp. 105-111. University of Amsterdam.
Nikolaev, N. and Smirnov, E.N. (2007). A One-Step Unscented
Particle Filter for Nonlinear Dynamical Systems, In: Proc. Int.
Conf. on Artificial Neural Networks, ICANN-2007, Porto, pp.747-756.
Smirnov, E.N., Sprinkhuizen-Kuyper, I.G.,
and N.I. Nikolaev (2006).
Generalizing Version
Space Support Vector Machines for Non-Separable Data. In: Proceedings of the
IEEE
International Workshop on
Reliability
Issues in Knowledge Discovery (RIKD'06),
IEEE Computer
Society, Hong Kong, China, December 18, 2006. pp.
744-748.
Smirnov, E.N. and A.M. Kaptein (2006).
Theoretical and
Experimental Study of a Meta-Typicalness Approach for Reliable
Classification. In: Proceedings of the
IEEE
International Workshop on
Reliability
Issues in Knowledge Discovery (RIKD'06),
IEEE Computer
Society, Hong Kong, China, December 18, 2006 , pp.
739-743.
Smirnov, E.N., Vanderlooy, S., and
Sprinkhuizen-Kuyper, I.G. (2006).
Meta-Typicalness Approach to Reliable Classification. In: Proceedings of the 17th
European Conference on Artificial Intelligence (ECAI 2006),
(pp.810-811), August
28 - September 1, Riva del Garda, Italy. [PDF]
Smirnov, E.N., Sprinkhuizen-Kuyper, I.G.,
Nalbantov, G.I., and Vanderlooy, S. (2006).
Version Space
Support Vector Machines. In:
Proceedings of the 17th European
Conference on Artificial Intelligence (ECAI 2006), (pp.809-810), August 28 -
September 1, Riva del Garda, Italy. [PDF]
Vanderlooy,S., Sprinkhuizen-Kuyper,I.G., and
Smirnov,E.N. (2006). An Analysis of
Reliable Classifiers through ROC Isometrics. To appear in
Proceedings of the ICML 2006 Workshop on
ROC Analysis (ROCML 2006), Pittsburgh, USA. [PDF]
Vanderlooy, S., Sprinkhuizen-Kuyper, I.G.,
and Smirnov, E.N. (2006).
Reliable
Classifiers in ROC Space. In
Proceedings of the 15th Annual Machine Learning Conference of Belgium
and the Netherlands (BENELEARN 2006) (pp. 113 - 120), May 11-12,
2006, Ghent, Belgium. [PDF]
Smirnov, E.N., Sprinkhuizen-Kuyper, I.G.,
and Nalbantov, G.I. (2006). Reliable
Classification with Version Spaces. In
Proceedings of the 25th SGAI
International Conference on Innovative techniques and Applications of
Artificial Intelligence (AI 2004), Springer, December, 2005,
Cambridge, UK, 288-304, 2006.
Sprinkhuizen-Kuyper, I.G., Smirnov, E.N., and Nalbantov, G.I.
(2005).
Reliability yields Information Gain.
Proceedings of the 14th Annual Machine
Learning Conference of Belgium and the Netherlands: BENELEARN 2005,
M. van Otterlo, M. Poel, and A. Nijholt (eds.), Februari 17-18 2005,
Enschede, University of Twente. ISSN 0929-0672, CTIT Workshop
Proceeding Series WP05-03, pp. 89-96. [PDF]
Smirnov, E.N., Sprinkhuizen-Kuyper, I.G.,
and Nalbantov, G. (2004).
Anonymous
Voting with Suport Vector Machines,
in: Proceedings of the Sixteenth Belgian-Dutch Conference on
Artificial Intelligence (BNAIC-04), 43-50.
[PDF]
Smirnov, E.N., Sprinkhuizen-Kuyper, I.G.,
and Van den Herik, H.J. (2004).
Unifying Version Space Representations: Part Two,
in: Proceedings of the Thirteenth Belgian-Dutch Conference on Machine
Learning (BENELEARN-04), 110-117. [PDF]
Wiesman, F., Graat, G., and Smirnov, E. (2003). Infomation
Markets for Agent-Based Retrieval, in: Proceedings of the Fifteenth
Dutch-Belgian Artificial Intelligence Conference, (BNAIC 2003), Nijmegen,
347-354, 2003.
Donkers, J., Van den Herik,H.J., Postma, E.O., Smirnov, E.N.,
Sprinkhuizen-Kuyper, I.G., and Verbeek, J. (2002).
Intelligent Data Analysis (IDA).
in: Book of abstracts of "Lerende Oplossingen", June 14 2002, Nijmegen, the Netherlands, Technologiestichting STW / Stichting Neurale
Netwerken, STW-2002 05 30-01, pp. 3-4.
Smirnov, E.N., Sprinkhuizen-Kuyper, I.G., and Van den
Herik, H.J. (2002). Efficient
Instance Retraction. In: D. Scott (ed.),
Proceedings of the
Tenth International Conference
Artificial Intelligence:
Methodology, Systems, and Applications, (AIMSA 2002),. LNAI
2443, Springer-Verlag, Berlin Heidelberg, pp. 21-30.
Smirnov, E.N., Sprinkhuizen-Kuyper, and van den, Herik,
H.J. (2002). New Version-Space
Representations for Efficient Instance Retraction.
in: M. Klemettinen, R. Meo, F. Giannotti, and L. De Raedt (eds.),
Proceedings of the First International
Workshop on Knowledge Discovery in Inductive Databases, KDID'02,
Helsinki University Printing House, Helsinki, pp. 32-49.
Smirnov, E.N., van den Herik, H.J., and Sprinkhuizen-Kuyper, I.G.
(2002).
Adaptable Boundary Sets, Seventh International Symposium on
Artificial Intelligence and Mathematics, AI&M 29-2002, January 2-4,
2002, Fort Lauderdale, Florida (http://rutcor.rutgers.edu/~amai/aimath02/PAPERS/42.pdf).
Smirnov, E.N., van den Herik, H.J., and
Sprinkhuizen-Kuyper, I.G. (2001). An Adaptable Version-Space Representation,
in: Proceedings of the Thirteenth Dutch-Belgian Artificial Intelligence
Conference, (BNAIC 2001), Universiteit van Amsterdam, 320-328.
Smirnov, E. N. and van den Herik, H.J.
(2000) Applying Preference
Biases to Conjunctive and Disjunctive Version Spaces, Proceedings of
the Ninth International Conference on Artificial Intelligence:
Methodology, Systems, and Applications (AIMSA-00), LNAI 1904, Springer,
321-330.
Smirnov, E.N., and Braspenning, P.J. (1999). Version Space
Convergence with Instance -Based Boundary Sets, in: Proceedings of
the Advanced Course on Artificial Intelligence (ACAI-99), Chania,
Greece, pp. 17-19.
Smirnov, E.N., and Braspenning, P.J., Description
Identification and Retraction with Integrated Instance/Concept-Based
Boundary Sets, in: Proceedings of the Eleventh Midwest Conference on
Artificial Intelligence, (MAICS-99), AAAI Press, April, 1999,
pp.100-107.
Smirnov, E.N., and Braspenning, P.J. (1998) Version Space Retraction
with Instance/Concept-Based Boundary Sets, in: Proceedings of the
Tenth Netherlands/ Belgium Conference on Artificial Intelligence,
(NAIC-98), Amsterdam, November, 97-106.
Smirnov, E.N., and Braspenning, P.J. (1998). Version Space Retraction
with Instance-Based Boundary Sets, in: Proceedings of the Seventh
International Conference on Artificial Intelligence: Methodology,
Systems and Applications, (AIMSA-98), LNAI 1480, Springer, 389 - 402.
Smirnov, E.N., and Braspenning, P.J. (1998) Generalised Version
Space Learning Algorithms, in: Proceedings of the Sixth Ibero-American
Conference on Artificial Intelligence, ( IBERAMIA-98), Colibri, Lisbon,
Portugal, 267 -278.
Smirnov, E.N., and Braspenning, P.J. (1998) Learning
Disjunctive Concepts Irrespective of the Example Ordering, in:
Proceedings of the First Workshop “Learning-98”, Madrid,
(http://alcaudon.tsc.uc3m.es/~learn98/papers/papers/paper011/)
Smirnov, E.N., and Braspenning, P.J. (1998). Description
Identification with Instance/Concept-Based Boundary Sets, in:
Proceedings of the Eight Belgian-Dutch Conference on Machine Learning,
(BENELEARN-98), Technical Report N 352, ATO-DLO, 62-70.
Smirnov, E.N.
and Braspenning, P. (1998). Version Space Learning with
Instance-Based Boundary Sets, in: Proceedings of the Thirteenth
Europen Conference on Artificial Intelligence, (ECAI-98), JohnWiley and
Sons, Ltd, 460-464, 1998. (The Best Paper Prize)
Neves, J., Smirnov, E. And Analide, C. (1997). Incremental Learning
of Inheritance, in: Proceedings of the Third International
Conference on Semiotics and Information Systems: Learning Perspectives,
Gaithersburg, USA, 137-146.
Smirnov, E.N., and Braspenning, P.J. (1997) A Space Fragmenting
Family of Version Space Algorithms, in: Proceedings of the Ninth
Dutch Conference on Artificial Intelligence, (NAIC-97), 313-322.
Smirnov,E.N. and Nikolaev, N.I.
(1996).
A Polynomially Bounded
Incremental Algorithm for Multiple EBL, in: Proceedings of the Fifth
Turkish Symposium on Artificial Intelligence and Neural Networks,
(TAINN-96), Istambul, Turkey, 120-128.
Nikolaev, N.I. and Smirnov, E.N. (1996). Stohastically Guided
Disjunctive Version Space Learning, in: Proceedings of the Twelfth
European Conference on Artificial Intelligence, (ECAI-96), JohnWiley and
Sons, Ltd, 443-448.
Smirnov,E.N. and Nikolaev, N.I. (1995). Incremental Learning with
Space Fragmenting 2, in: Proceedings of the Fourth Turkish Symposium
on Artificial Intelligence and Neural Networks, (TAINN-95), Marmara,
Turkey, 80-88.
Smirnov, E.N. and Nikolaev, N.I. (1995). Multiple
Explanation-Based Learning Guided by Space Fragmenting, in:
Proceedings of the Fifth Scandinavian Conference on Artificial
Intelligence, (SCAI-95), IOS Press, Amsterdam, 85-96.
Nikolaev, N.I. and Smirnov, E.N.
(1995).
Analytical Learning Guided
by Empirical Technology: An Approach to Integration, in: Proceedings
of the Eighth European Conference on Machine Learning, (ECML-95), Spring
Verlag, 327-330.
Smirnov, E.N. and Nikolaev, N.I.
(1994).
Intelligent Tutoring by
Knowledge Refinement with Version Space, in: Proceedings of the
Fourth International Conference of Aspects of Advanced Manufacturing &
Hybrid Automation, IOS Press, Amsterdam, 247-251.
Smirnov,E.N. and Nikolaev, N.I.
(1994).
Multiple Explanation-Based
Learning Guided by the Candidate Elimination Algorithm, in: P.
Jorrand (ed.), Artificial Intelligence VI: Methodology, System,
Application, World Scientific Publ., 247-256.
Smirnov, E.N.
(1993). Combining Empirical and Analytical Learning
with Space Fragmenting and Explanation Based Generalization, in:
Proceedings of 18 International Conference "Information Technologies and
Programming", Pedagoc Press, Sofia, 120-129.
|