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Markov Models for Pattern Recognition From Theory to Applications /
Markov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - cover...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edición: | 1st ed. 2008. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-540-71770-6 | ||
| 003 | DE-He213 | ||
| 005 | 20220119174656.0 | ||
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| 008 | 100301s2008 gw | s |||| 0|eng d | ||
| 020 | |a 9783540717706 |9 978-3-540-71770-6 | ||
| 024 | 7 | |a 10.1007/978-3-540-71770-6 |2 doi | |
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| 100 | 1 | |a Fink, Gernot A. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Markov Models for Pattern Recognition |h [electronic resource] : |b From Theory to Applications / |c by Gernot A. Fink. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2008. | |
| 300 | |a XII, 248 p. 51 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 505 | 0 | |a Application Areas -- Application Areas -- Theory -- Foundations of Mathematical Statistics -- Vector Quantization -- Hidden Markov Models -- n-Gram Models -- Practice -- Computations with Probabilities -- Configuration of Hidden Markov Models -- Robust Parameter Estimation -- Efficient Model Evaluation -- Model Adaptation -- Integrated Search Methods -- Systems -- Speech Recognition -- Character and Handwriting Recognition -- Analysis of Biological Sequences. | |
| 520 | |a Markov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - covering Hidden Markov models and Markov chain models - as used for sequential data and presents the techniques necessary to build successful systems for practical applications. This comprehensive introduction to the Markov modeling framework describes the underlying theoretical concepts - covering Hidden Markov models and Markov chain models - and presents the techniques and algorithmic solutions essential to creating real world applications. The actual use of Markov models in their three main application areas - namely speech recognition, handwriting recognition, and biological sequence analysis - is presented with examples of successful systems. Encompassing both Markov model theory and practise, this book addresses the needs of practitioners and researchers from the field of pattern recognition as well as graduate students with a related major field of study. | ||
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Computer Vision. |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642090882 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540837077 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540717669 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-540-71770-6 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||