On classification improvement by using an approximate discriminative hidden Markov model
Fuente:
Redalyc
Saved in:
| Main Author: | |
|---|---|
| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidad de Antioquia
2010
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1876472781715013632 |
|---|---|
| author | Johanna Carvajal- González |
| author_facet | Johanna Carvajal- González |
| contents | On classification improvement by using an approximate discriminative hidden Markov model Johanna Carvajal- González Milton Sarria-Paja Germán Castellanos- Domínguez Ingeniería MMI biosignals Hidden Markov models discriminative training HMMs are statistical models used in a very successful and effective form in speech recognition. However, HMM is a general model to describe the dynamic of stochastic processes; therefore it can be applied to a huge variety of biomedical signals. Usually, the HMM parameters are estimated by means of MLE (Maximum Likelihood Estimation) criterion. Nevertheless, MLE has as disadvantage that the distribution it is wanted to adjust is the distribution of each class, besides the models and/or data of other classes do not participate in the parameter re-estimation, as a result, the ML criterion is not directly related to reduce the error rate; it has led to many researchers to choice other training techniques known as discriminative training, including maximum mutual information (MMI) estimation. In this work, we carry out an EEG classification in order to compare HMM trained with both ML estimation and MMI estimation. The obtained results show a better performance in all database used. 2010 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43019328018 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.55 |
| format | Artículo científico |
| id | redalyc_43019328018 |
| institution | Redalyc |
| language | en |
| publishDate | 2010 |
| publisher | Universidad de Antioquia |
| spellingShingle | On classification improvement by using an approximate discriminative hidden Markov model Johanna Carvajal- González Ingeniería MMI biosignals Hidden Markov models discriminative training On classification improvement by using an approximate discriminative hidden Markov model Johanna Carvajal- González Milton Sarria-Paja Germán Castellanos- Domínguez Ingeniería MMI biosignals Hidden Markov models discriminative training HMMs are statistical models used in a very successful and effective form in speech recognition. However, HMM is a general model to describe the dynamic of stochastic processes; therefore it can be applied to a huge variety of biomedical signals. Usually, the HMM parameters are estimated by means of MLE (Maximum Likelihood Estimation) criterion. Nevertheless, MLE has as disadvantage that the distribution it is wanted to adjust is the distribution of each class, besides the models and/or data of other classes do not participate in the parameter re-estimation, as a result, the ML criterion is not directly related to reduce the error rate; it has led to many researchers to choice other training techniques known as discriminative training, including maximum mutual information (MMI) estimation. In this work, we carry out an EEG classification in order to compare HMM trained with both ML estimation and MMI estimation. The obtained results show a better performance in all database used. 2010 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43019328018 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.55 |
| title | On classification improvement by using an approximate discriminative hidden Markov model |
| topic | Ingeniería MMI biosignals Hidden Markov models discriminative training |
| url | https://www.redalyc.org/articulo.oa?id=43019328018 |