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A Hidden Markov Model with Controlled Non-parametric Emissions

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Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9692))

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Abstract

A novel nonparametric model is introduced to model and control emission densities of a non-ergodic hidden Markov model. Having both multiclass and one-class classifications simultaneously, for recognizing the best match between multiple classes and then accepting or rejecting the given input pattern, is the major characteristic of this algorithm. Also, since the proposed method creates independent feature spaces and trains by positive samples only, it allows the vocabulary of trained patterns to grow without any concern about growing into a negative set (which is a problem with algorithms that use negative/garbage sets for binary training).

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Author information

Authors and Affiliations

  1. Qualcomm Inc., 105 Commerce Valley Drive West, Markham, ON, Canada

    Atid Shamaie

Authors
  1. Atid Shamaie
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Corresponding author

Correspondence to Atid Shamaie .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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© 2016 Springer International Publishing Switzerland

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Shamaie, A. (2016). A Hidden Markov Model with Controlled Non-parametric Emissions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_54

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  • DOI: https://doi.org/10.1007/978-3-319-39378-0_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39377-3

  • Online ISBN: 978-3-319-39378-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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