- Nikolaev, Nikolay; Tino, Peter and Smirnov, Evgueni. 2011. Time-Dependent Series Variance Estimation via Recurrent Neural Networks. Artificial Neural Networks and Machine Learning 鈥� ICANN 2011, 6791(n/a), pp. 176-184. ISSN 0302-9743
- Mirikitani, Derrick and Nikolaev, Nikolay. 2011. Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks. Neural Computing and Applications, 20(1), pp. 79-89. ISSN 0941-0643
- Mirikitani, D. T. and Nikolaev, Nikolay. 2010. Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling. IEEE Transactions on Neural Networks, 21(2), pp. 262-274. ISSN 1045-9227
- Smirnov, Evgueni; Nalbantov, Georgi and Nikolaev, Nikolay. 2010. k-Version-Space Multi-class Classification Based on k-Consistency Tests. European Conference on Machine Learning and Knowledge Discovery in Databases, 6323, pp. 277-292. ISSN 0302-9743
- Smirnov, Evgueni; Nikolaev, Nikolay and Nalbantov, Georgi. 2010. Single-Stacking Conformity Approach to Reliable Classification. Artificial Intelligence: Methodology, Systems, and Applications, 6304, pp. 161-170. ISSN 0302-9743
- Mirikitani, Derrick T. and Nikolaev, Nikolay. 2010. Efficient online recurrent connectionist learning with the ensemble Kalman filter. Neurocomputing, 73(4 - 6), pp. 1024-1030. ISSN 0925-2312
- Nikolaev, Nikolay and de Menezes, L. 2008. Sequential Bayesian Kernel Modelling with Non-Gaussian Noise. Neural Networks, 21(1), pp. 36-47. ISSN 0893-6080
- Nikolaev, Nikolay. 2003. Polynomial Harmonic GMDH Learning Networks for Time Series Modeling. Neural Networks, 16(10), pp. 1527-1540. ISSN 08936080
- Nikolaev, Nikolay. 2003. Learning polynomial feedforward neural networks by genetic programming and backpropagation. IEEE Transactions on Neural Networks, 14(2), pp. 337-350. ISSN 10459227
- Nikolaev, Nikolay and Iba, Hitoshi. 2002. Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform. International Journal of Neural Systems, 12(5), pp. 399-410. ISSN 0129-0657
- Nikolaev, Nikolay and Iba, Hitoshi. 2001. Accelerated Genetic Programming of Polynomials. Genetic Programmimg and Evolvable Machines, 2(3), pp. 231-257. ISSN 1389-2576
- Nikolaev, Nikolay. 2001. Regularization Approach to Inductive Genetic Programming. IEEE Transactions on Evolutionary Computation, 5(4), pp. 359-375. ISSN 1089778X
Dr Nikolay Nikolaev
Nikolay’s recent work is devoted to genetic programming of tree-structured polynomials.
Staff details
Areas of supervision
Evolutionary computation, genetic algorithms & genetic programming, neural networks, biocomputation, machine learning, applications to time-series prediction, financial engineering & data mining.
Featured works
Book chapters and conference papers
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., 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.
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.
Publications and research outputs
Book
- Nikolaev, Nikolay and Iba, H.. 2006. Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods. Springer. ISBN 0387312390
Book Section
- Nikolaev, Nikolay; De Menezes, L. M. and Smirnov, E.. 2014. Nonlinear filtering of asymmetric stochastic volatility models and Value-at-Risk estimation. In: R. J. Almeida; D. Maringer; V. Palade and A. Serguieva, eds. IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr). IEEE, pp. 310-317. ISBN 978-147992380-9
Article
- Nikolaev, Nikolay; Smirnov, Evgueni; Stamate, Daniel and Zimmer, Robert. 2019. A Regime-Switching Recurrent Neural Network Model Applied to Wind Time Series. Applied Soft Computing, 80, pp. 723-734. ISSN 1568-4946
- Nikolaev, Nikolay; Peter, Tino and Evgueni, Smirnov. 2013. Time-dependent series variance learning with recurrent mixture density networks. Neurocomputing, 122, pp. 501-512. ISSN 0925-2312
- Nikolaev, Nikolay; Boshnakov, Georgi N. and Zimmer, Robert. 2013. Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation. Expert Systems with Applications, 40(6), pp. 2233-2243. ISSN 0957-4174
Conference or Workshop Item
- Vanegdom, A.; Nikolaev, Nikolay and Garagnani, M.. 2022. 'Standard feedforward neural networks with backprop cannot support cognitive superposition'. In: Bernstein Conference 2022. Berlin, Germany 13-16 September 2022.
Research Interests
Neural networks
statistical learning networks, basis-function networks, constructive learning of the topology and initial weights of
multilayer neural networks; financial engineering by basis-function neural networks; chaotic time-series prediction by
statistical networks.
Genetic Algorithms
Structured genetic algorithms with cooperative subpopulations flowing on fitness sublandscapes; Fourier expansions
of fitness landscapes over regular graphs, messy genetic algorithms for applied economic regression tasks.
Inductive Genetic Programming (iGP):
Evolutionary induction of multivariate high-order polynomials, genetic programming of statistical learning networks,
genetic programming of polynomial discriminant classifiers, regularization in iGP, finite-state automata induction by
iGP.
Data mining
A utomated discovery of polynomials from data with numerical and continuous features; sequential forward and
backward feature selection for construction of multi-layer neural networks.
Machine Learning
Decision tree classifiers, stochastic complexity (Minimum Description Length-MDL) measures for decision tree
learners, multivariate splitting methods for non-linear decision trees; linear and oblique decision trees,
distance-based decision trees.
Current research
My recent work is devoted to genetic programming of tree-structured polynomials, known as statistical learning networks of the
GMDH type. This includes design of stochastic complexity (Minimum Description Length-MDL) and statistical
fitness functions for efficient search navigation. These functions are elaborated using ideas from the
regularization theory aiming at evolution of parsimonious, accurate and predictive polynomials.