Nikolay Nikolaev, Goldsmiths College, University of London

Recent publications

Book

Journal papers

  • Nikolaev,N., Tino,P. and Smirnov,E. (2013). Time-dependent Series Variance Learning with Recurrent Mixture Density Networks, Neurocomputing, vol.122, pp.501512. (www.sciencedirect.com).
  • Nikolaev,N., Boshnakov,G. and Zimmer,R. (2013). Heavy-tail Mixture GARCH Volatility Modeling and Value-at-Risk Estimation, Expert Systems with Applications, vol.40, N:6, pp.2233-2243. (www.sciencedirect.com).
  • Mirikitani,D. and Nikolaev,N. (2011). Nonlinear Maximum Likelihood Estimation of Electricity Spot Prices using Recurrent Neural Networks, Neural Computing and Applications, vol.20, N:1, pp.79-89. (www.springerlink.com)
  • Mirikitani,D. and Nikolaev,N. (2010). Recursive Bayesian Recurrent Neural Networks for Time Series Modeling, IEEE Transactions on Neural Networks, vol.21, N: 2, pp.262-274. (ieeexplore.ieee.org).
  • Mirikitani,D. and Nikolaev,N. (2010). Efficient Online Recurrent Connectionist Learning with the Ensemble Kalman Filter, Neurocomputing, vol.73,N: 4-6, pp.1024-1030. (www.sciencedirect.com)
  • Nikolaev,N. and de Menezes,L. (2008). Sequential Bayesian Kernel Modelling with Non-Gaussian Noise, Neural Networks, vol.21. N: 1, pp.36-47. (www.sciencedirect.com).
  • de Menezes,L. and Nikolaev,N. (2005). Forecasting with Genetically Programmed Polynomial Neural Networks, International Journal of Forecasting, vol.22. N: 2, pp.249-265. (www.sciencedirect.com).
  • Nikolaev,N. and Iba,H. (2003). Polynomial Harmonic GMDH Learning Networks for Time Series Modeling, Neural Networks, vol.16, N:10, pp.1527-1540. (www.sciencedirect.com).
  • Nikolaev,N. and Iba,H. (2003). Learning Polynomial Feedforward Neural Networks by Inductive Genetic Programming and Backpropagation, IEEE Transactions on Neural Networks, vol.14, N:2, pp.337-350. (ieeexplore.ieee.org).
  • Nikolaev,N. and Iba,H. (2002). Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform, International Journal of Neural Systems, vol.12, N:5, pp.399-410. (www.worldscinet.com).
  • Nikolaev,N. and Iba,H. (2001). Accelerated Genetic Programming of Polynomials, Genetic Programmimg and Evolvable Machines, Kluwer Academic Publ., vol.2, N:3, pp.231-257.
  • Nikolaev,N. and Iba,H. (2001). Regularization Approach to Inductive Genetic Programming, IEEE Transactions on Evolutionary Computation, vol.5, N:4, pp.359-375.
  • Nikolaev,N. and Slavov, V. (1998). Inductive Genetic Programming with Decision Trees. Intelligent Data Analysis: An International Journal, Elsevier Science Inc., New York, vol.2, No.1, pp. 31-44.

Book chapters

  • Nikolaev,N., and Smirnov,E. (2012). Error Bars for Polynomial Neural Networks. In: Liu,J.N.K., Smirnov,E. and Dai,H.(Eds.), Reliable Knowledge Discovery, Springer, pp.51-66.
  • Nikolaev,N., and Iba,H. (2002). Genetic Programming of Polynomial Models for Financial Forecasting. In: Shu-Heng Chen (Ed.), Genetic Algorithms and Genetic Programming in Computational Finance, Chapter 5, Kluwer Academic Publ., Boston, MA, pp.103-123.
  • Nikolaev,N., Iba,H., and Slavov,V. (1999). Inductive Genetic Programming with Immune Network Dynamics. In: L.Spector, W.B.Langdon, U.-M. O'Reilly and P.J.Angeline (Eds.), Advances In Genetic Programming 3, Chapter 15, MIT Press, Cambridge, MA, pp.355-376.
  • Slavov,V. and Nikolaev,N. (1999). Genetic Algorithms, Fitness Sublandscapes and and Subpopulations. In: C.Reeves and W.Banzhaf (Eds.), Foundations of Genetic Algorithms, FOGA-5, Morgan Kaufmann, CA, pp.199-218.

Conference proceedings

2014

  • Nikolaev,N., de Menezes,L. and Smirnov,E. (2014). Nonlinear Filtering of Asymmetric Stochastic Volatility Models and Value-at-Risk Estimation, In: Proc. IEEE Conf. Computational Intelligence for Financial Engineering and Economics (CIFEr-2014), London.[slides]

2012

  • Nikolaev,N. and Smirnov,E. (2012). Analytical Factor Stochastic Volatility Modeling for Portfolio Allocation, In: R.Yager and R.Golan (Eds.) Proc. IEEE Conf. Computational Intelligence for Financial Engineering and Economics (CIFEr-2012), New York, pp.78-85.

2011

  • Nikolaev,N., Tino,P. and Smirnov,E.N. (2011). Time-Dependent Series Variance Estimation via Recurrent Neural Networks, In: T.Honkela et al (Eds.) Proc. Int. Conf. on Artificial Neural Networks, ICANN-2011, Espoo, Finland, LNCS-6971, Springer, pp.176-184.

2010

  • Smirnov,E.N., Nalbantov,G.I. and Nikolaev,N. (2010). k-Version-Space Multi-class Classification Based on k-Consistency Tests, In: Proc. European Conf. on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2010, Barcelona, Spain, LNCS-6323, Springer, pp.277-292.
  • Smirnov,E.N., Nikolaev,N. and Nalbantov,G.I. (2010). Single-Stacking Conformity Approach to Reliable Classification, In: Dicheva and Dochev (Eds.): Proc. 14th Int. Conf. Artificial Intelligence: Methodology, Systems, and Applications AIMSA 2010, LNCS 6304, Springer, pp.161-170.

2009

  • 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.

2008

  • Smirnov,E., Nikolaev,N. and Nalbantov, G. (2008). Description Identification and the Consistency Problem, In: M.Bramner, F.Coenen and M.Petridis (Eds.), Research and Development in Intelligent Systems XXV, Proceedings of AI-2008 Conference, Cambridge, pp.61-74.
  • Mirikitani,D. and Nikolaev,N. (2008). Recurrent Expectation Maximization Neural Modeling, In: Proc. Int. Conf. Computational Intelligence for Modelling Control and Automation, CIMCA-2008, Vienna, Austria, pp.674-679.

2007

  • Nikolaev,N. and Smirnov, E. (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.
  • Mirikitani,D. and Nikolaev,N. (2007). Recursive Bayesian Levenberg-Marquardt Training of Recurrent Neural Networks, In: Proc. Int. Joint Conference on Neural Networks, IJCNN-2007, Orlando, FL, pp.1089-1098.

2005

  • Nikolaev,N. and Tino, P. (2005). Sequential Relevance Vector Machine Learning from Time Series, In: Proc. Int. Joint Conference on Neural Networks, IJCNN-2005, Montreal, CA, pp.1308-1313.
  • Tino,P., Nikolaev,N. and Yao,X. (2005). Volatility Forecasting with Sparse Bayesian Kernel Models, In: Proc. 4th International Conference on Computational Intelligence in Economics and Finance, Salt Lake City, UT, pp.1150-1153.

2002

  • de Menezes,L. and Nikolaev,N. (2002). Confidence Intervals for Polynomial Neural Network Models, In: Proc. 22nd Int. Symposium on Forecasting, ISF-2002, Dublin, Ireland.
  • Nikolaev,N., de Menezes,L. and Iba, H. (2002). Overfitting Avoidance in Genetic Programming of Polynomials, In: Proc. 2002 Congress on Evolutionary Computation, CEC2002, IEEE Press, Piscataway, NJ, pp.1209-1214.

2001

  • Nikolaev,N. and Iba, H. (2001). Genetic Programming using Chebishev Polynomials, In: L.Spector, E.D.Goodman, A.Wu, W.B.Langdon, H.-M.Voigt, M.Gen, S.Sen, M.Dorigo, S.Pezeshk, M.H.Garzon, and E.Burke (Eds.), Proc. of the Genetic and Evolutionary Computation Conference, GECCO-2001, Morgan Kaufmann Publ., San Francisco, CA, pp.89-96.
  • Nikolaev,N. and Iba, H. (2001). Genetic Programming of Polynomial Harmonic Models using the Discrete Fourier Transform, In: Proc. 2001 Congress on Evolutionary Computation, CEC2001, IEEE Press, Piscataway, NJ, pp.267-274.
  • de Menezes,L. and Nikolaev,N. (2001). Forecasting with Genetically Programmed Polynomial Networks, In: Proc. of the Int. Symposium on Forecasting, ISF-2001, Atlanta, GA, June 17-20.

2000

  • Iba, H. and Nikolaev,N. (2000). Genetic Programming Polynomial Models of Financial Data Series, In: Proc. of 2000 Congress on Evolutionary Computation, CEC-2000, IEEE Press, pp.1459-1466.
  • Iba, H. and Nikolaev,N. (2000). Financial Data Prediction by Means of Genetic Programming, In: Proc. of the Sixth Intern. Conf. on Computing in Economics and Finance, CEF2000, Barcelona, Spain, July 6-8.
  • Nikolaev,N. and Iba,H. (2000). Inductive Genetic Programming of Polynomial Learning Networks, In: X. Yao (Ed.), Proc. of the IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, ECNN-2000, IEEE Press, pp.158-167.

1999

  • Nikolaev,N. and Iba,H. (1999). Automated Discovery of Polynomials by Inductive Genetic Programming, In: J.M. Zytkow and J. Rauch (Eds.) Principles of Data Mining and Knowledge Discovery. Third European Conference, PKDD '99, LNAI-1704, Springer, Berlin, pp. 456-461.

1998

  • Slavov,V. and Nikolaev, N. (1998). Immune Network Dynamics for Inductive Problem Solving, In: A.E. Eiben, T.Back, M.Schoenauer, and H.-P. Schwefel (Eds.) Parallel Problem Solving from Nature, PPSN V, LNCS-1498, Springer, Berlin, pp.712-721.
  • Nikolaev,N. and Slavov,V. (1998). The Dynamics of Biased Inductive Genetic Programming, In: J. Koza et al. (Eds.) Proc. Third Annual Conference on Genetic Programming, GP-98, Morgan Kaufmann, CA, pp.260-268.
  • Nikolaev,N. and Slavov,V. (1998). Concepts of Inductive Genetic Programming, In: W. Banzhaf, R. Poly, M. Schoenauer and T. Fogarty (Eds.), EuroGP'98: First European Workshop on Genetic Programming, LNCS-1391, Springer, Berlin, pp. 49-59.
  • Slavov,V. and Nikolaev,N. (1998). Fitness Landscapes and Inductive Genetic Programming. In: G. Smith, N.C. Steele, and R.F. Albrecht (Eds.), Third Int. Conference on Artificial Neural Networks and Genetic Algorithms, ICANNGA'97, Springer Verlag, Wien, pp. 414-418.

1997

  • Slavov,V. and Nikolaev,N. (1997). Fitness Sublandscapes in Evolutionary Automata Induction, In: Honavar (ed.), Proc. ICML-97 Workshop on Automata Induction, Grammatical Inference and Language Acquisition, Vanderbilt University, TN.
  • Slavov,V. and Nikolaev,N. (1997). Inductive Genetic Programming and the Superposition of Fitness Landscapes, In: Th. Baeck (ed.), Proc. of the Seventh Int. Conf. on Genetic Algorithms, ICGA-97, Michigan, pp. 97-104.
  • Nikolaev,N. and Slavov,V. (1997). Inductive Genetic Programming with Decision Trees, In: M. van Someren and G.Widmer (eds.), Machine Learning: ECML-97, Ninth European Conf. on Machine Learning, LNAI 1224, Springer, Berlin, pp. 183-190.
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