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Some Notes on Computational and Theoretical Issues in Artificial Intelligence and Machine Learning

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Advances in Neural Networks (WIRN 2015)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

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Abstract

In the attempt to implement applications of public utility which simplify the user access to future, remote and nearby social services, new mathematical models and new psychological and computational approaches from existing cognitive frameworks and algorithmic solutions have been developed. The nature of these instruments is either deterministic, probabilistic, or both. Their use depends upon their contribute to the conception of new ICT functionalities and evaluation methods for modelling concepts of learning, reasoning, and data interpretation. This introductory chapter provide a brief overview on the theoretical and computational issues of such artificial intelligent methods and how they are applied to several research problems.

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Notes

  1. 1.

    NP-complete and NP-hard problems, where NP indicates that the problem has a Non Polynomial solution either in terms of computational time or of memory occupancy, or both.

References

  1. Esposito, A.: The importance of data for training intelligent devices. In: Apolloni, B., Kurfess, C. (eds) From Synapses to Rules: Discovering Symbolic Knowledge from Neural Processed Data, pp. 229–250. Kluwer Academic press (2002)

    Google Scholar 

  2. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  3. Herzog, A., Pastor, P., Kalakrishnan, M., et al.: Template-based learning of grasp selection. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2379– 2384, Saint Paul, MN (USA) (2012)

    Google Scholar 

  4. Gutkin, A., King, S.: Inductive string template-based learning of spoken language (2005). https://www.era.lib.ed.ac.uk/handle/1842/932

  5. Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley (2009). ISBN: 978-0-470-51706-2

    Google Scholar 

  6. Cacciola, M., Calcagno, S., Morabito, F.C., Versaci, M.: Computational intelligence aspects for defect classification in aeronautic composites by using ultrasonic pulse. IEEE Trans. Ultrason. Ferroelectr. Freq Control, 55(4), 870–878 (2008)

    Google Scholar 

  7. Yuen, C.T., Rizon, M., San, W.S., Seong, T.C.: Facial features for template matching based face recognition. Am. J. Eng. Appl. Sci. 3(1) 899–903 (2010)

    Google Scholar 

  8. Tahmasebi, P., Hezarkhani, A., Sahimi, M.: Multiple-point geostatistical modeling based on the cross-correlation functions. Comput. Geosci. 16(3), 779–797 (2012)

    Article  Google Scholar 

  9. De Wachter, M., Matton, M., et al.: Template-based continuous speech recognition. IEEE Trans. Audio Speech Lang. Process. 15(4), 1377–1390 (2007)

    Article  Google Scholar 

  10. Sniedovich, M.: Dynamic Programming: Foundations and principles. Taylor & Francis (2010)

    Google Scholar 

  11. Rabiner, L.R., Levinson, S.E.: Isolated and connected word recognition: theory and selected applications. IEEE Trans. Commun. 29(5), 621–659 (1981)

    Article  Google Scholar 

  12. Goldinger, S.D.: Words and voices: episodic traces in spoken word identification and recognition memory. J. Exp. Psychol. Learn. Memory Cogn. 33, 1166–1183 (1996)

    Article  Google Scholar 

  13. Jacoby, L., Hayman, C.: Specific visual transfer in word identification. J. Exp. Psychol. Learn. Memory Cogn. 13, 456–463 (1987)

    Article  Google Scholar 

  14. Esposito, A., Esposito, A.M.: On speech and gesture synchrony. In: Esposito, A., et al. (eds.) Analysis of Verbal and Nonverbal Communication and Enactment: The Processing Issue. LNCS vol. 6800, pp. 252–272. Springer, Heidelberg (2011). ISBN: 978-3-642-25774-2

    Google Scholar 

  15. Esposito, A., Marinaro, M.: What pauses can tell us about speech and gesture partnership. In: Esposito, A., et al. (eds) Fundamentals of Verbal and Nonverbal Communication and the Biometric Issue. NATO Publishing Series, Human and Societal Dynamics, vol. 18,pp. 45−57. IOS press, The Netherlands (2007)

    Google Scholar 

  16. Aradilla, G., Vepa, J., Bourlard, H.: Improving speech recognition using a data-driven approach. In: Proceedings of EUROSPEECH, Lisbon, pp. 3333−3336 (2005)

    Google Scholar 

  17. De Wachter, M., Demuynck, K., Wambacq, P., Van Compernolle, D.: A locally weighted distance measure for example based speech recognition. In Proceedings of ICASSP, pp. 181–184, Montreal, Canada (2004)

    Google Scholar 

  18. Hawkins.: Contribution of fine phonetic detail to speech understanding. In: Proceedings of 15th International Congress of Phonetic Sciences (ICPhS03), pp. 293–296, Barcelona, Spain (2003)

    Google Scholar 

  19. Maier, V., Moore, R.K.: An investigation into a simulation of episodic memory for automatic speech recognition. In: Proceedings of Interspeech-2005, pp. 1245–1248, Lisbon (2005)

    Google Scholar 

  20. Matton, M., De Wachter, M., Van Compernolle, D., Cools, R.: Maximum mutual information training of distance measures for template based speech recognition. In: Proceedings of International Conference on Speech and Computer, pp. 511−514, Patras, Greece (2005)

    Google Scholar 

  21. Strik, H.: How to handle pronunciation variation in ASR: by storing episodes in memory? In: Proceedings of ITRW on Speech Recognition and Intrinsic Variation (SRIV2006), pp. 33–38, Toulouse, France (2006)

    Google Scholar 

  22. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    Google Scholar 

  23. Morabito, F.C.: Independent component analysis and feature extraction for NDT data. Mater. Eval. 58(1), 85–92 (2000)

    Google Scholar 

  24. Labate, D., La Foresta, F., Morabito, G., Palamara, I., Morabito, F.C.: On the use of empirical mode decomposition (EMD) for Alzheimer’s disease diagnosis. In: Smart Innovation, Systems and Technologies, vol. 37, pp. 121−128 (2015)

    Google Scholar 

  25. Aversano, G., Esposito, A.: Automatic parameter estimation for a context-independent speech segmentation algorithm. In: Sojka, P., et al. (eds) Text Speech and Dialogue, LNAI 2448, 293–300. Springer, Heidelberg (2002)

    Google Scholar 

  26. Esposito, A., Aversano, G.: Text independent methods for speech segmentation. In: Chollet, G., et al. (eds) Nonlinear Speech Modeling and Applications. Lectures Notes in Computer Science, vol. 3445, pp. 261–290. Springer, Heidelberg (2005)

    Google Scholar 

  27. Atassi, H., Smékal, Z., Esposito, A.: Emotion recognition from spontaneous Slavic speech. In: Proceedings of 3rd IEEE Interernational Conference on Cognitive Infocommunications (CogInfoCom2012), pp. 389–394, Kosice, Slovakia (2012)

    Google Scholar 

  28. Pudil, P., Novovicová, J., Kittler, K.: Floating search methods in feature selection. Patt. Recogn. Lett. 15(11), 1119–1125 (1994)

    Google Scholar 

  29. Atassi, H., Esposito, A: Speaker independent approach to the classification of emotional vocal expressions. In: Proceedings of IEEE Conference on Tools with Artificial Intelligence (ICTAI 2008), vol. 1, pp. 487–494, Dayton, OH, USA, 3−5 Nov 2008

    Google Scholar 

  30. Bengio, Y.: learning deep architectures for AI. In: Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127 (2009)

    Google Scholar 

  31. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  32. Mammone, N., Labate, D., Lay-Ekuakille, A., Morabito, F.C.: Analysis of absence seizure generation using EEG spatial-temporal regularity measures, Int. J. Neural Syst. 22(6), art. no. 1250024 (2012)

    Google Scholar 

  33. Morabito, F.C., Campolo, M., Labate, D., Morabito, G., Bonanno, L., Bramanti, A.: De Salvo, S., Bramanti, P.: A longitudinal EEG study of Alzheimer’s disease progression based on a complex network approach. Int. J. Neural Syst. 25(2), art.no. 1550005 (2015)

    Google Scholar 

  34. Schuller, B.: Deep learning our everyday emotions: a short overview. In: Bassis, et al. (eds) Advances in Neural Networks: Computational and Theoretical Issues. SIST Series, vol. 37, pp. 339–346. Springer, Heidelberg (2015)

    Google Scholar 

  35. Frankel, J., King, S.: Speech recognition using linear dynamic models. IEEE Trans. Audio Speech Lang. Process. 15(1), 246–256 (2007)

    Article  Google Scholar 

  36. Lee, C.H.: On automatic speech recognition at the dawn of the 21st century. IEICE Trans. Inf. Syst. E86-D (3) 377–396 (2003). Special Issue on Speech Information Processing

    Google Scholar 

  37. Wong, K.C., Chan, T.M., Peng, C., Li, Y., Zhang, Z.: DNA motif elucidation using belief propagation. Nucleic Acids Res. 41(16), e153 (2013)

    Article  Google Scholar 

  38. Nicolai, C., Sachs, F.: Solving ion channel kinetics with the Qub software. Biophys. Rev. Lett. 08, 191–211 (2013)

    Article  Google Scholar 

  39. Boudaren, M.Y., Monfrini, E., Pieczynski, W.: Unsupervised segmentation of random discrete data hidden with switching noise distributions. IEEE Signal Process. Lett. 19(10), 619–622 (2012)

    Article  Google Scholar 

  40. Fink, GA.: Markov Models for Pattern Recognition—From Theory to Applications. Advances in Computer Vision and Pattern Recognition, pp. 1–253. Springer (2014)

    Google Scholar 

  41. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  42. Khaitan, S.K., McCalley, J.D.: Design techniques and applications of cyberphysical systems: a survey. IEEE Syst. J. 9(2), 350–365 (2014)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Psicologia and IIASS, Seconda Università di Napoli, Caserta, Italy

    Anna Esposito

  2. Università di Milano, Milan, Italy

    Simone Bassis

  3. Università degli Studi “Mediterranea” di Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy

    Eros Pasero

Authors
  1. Anna Esposito
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  2. Simone Bassis
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  3. Francesco Carlo Morabito
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  4. Eros Pasero
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Corresponding author

Correspondence to Anna Esposito .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Esposito, A., Bassis, S., Morabito, F.C., Pasero, E. (2016). Some Notes on Computational and Theoretical Issues in Artificial Intelligence and Machine Learning. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-33747-0_1

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