Questions of "viability" evaluation of innovation projects are considered in this article. As a method of evaluation Hidden Markov Models are used. Problem of determining model parameters, which reproduce test data with highest accuracy are solving. For training the model statistical data on the implementation of innovative projects are used. Baum-Welch algorithm is used as a training algorithm.
References
Basics of Innovation Management: Theory and practice: tutorial / Edited by. P.N. Zavalina and al. – M.: JSC «NPO «Publishing house «Economy», 2000. – 475p. (In Russian)
Adizes I. Corporate lifecycles: how and why corporations grow and die and what to do about it / I. Adizes, Englewood Cliffs, N.J.: Prentice Hall, 1988
Russian federal program "UMNIK". [Online]. Available: http://www.fasie.ru/programs/programma-umnik/
Russian federal program "START". [Online]. Available: http://www.fasie.ru/programs/programma-start/
Russian federal program "UMNIK to START". [Online]. Available: http://www.nauka-nov.ru/fsrmpnts/665/
Dynkin E.B. Foundations of the theory of Markov processes / Dynkin E.B. - Moscow: FIZMALIT, 1992. - 228 p. (In Russian)
Elms A.J., Procter S., Illingworth J. The advantage of using an HMM-based approach for faxed word recognition// International Journal on Document Analysis and Recognition (1998) 1: 18–36
Rabiner L.P. Hidden Markov models and their use in favorites applications in speech recognition: Overview//TIIER, т.77, N2, february 1989 — p. 86–120.
Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). 2006
Geoffrey McLachlan and Thriyambakam Krishnan. The EM Algorithm and Extensions. John Wiley & Sons, New York, 1996.
Jeff A. Bilmes. A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report TR-97-021, U. C. Berkeley, April 1998.
Welch L. Hidden Markov Models and the Baum-Welch algorithm // IEEE Information Theory Society Newsletter, 2003.