Abstract
In this paper, we propose a simple algorithm that takes multidimensional neural input data and decomposes the joint likelihood into marginals using Boosted Mixtures of Hidden Markov Chains (BM-HMM). The algorithm applies techniques from boosting to create hierarchal dependencies between these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is weighted and incorporated into an ensemble decision. Our results show that this algorithm is very simple to train and computationally efficient, while also providing the ability to reduce the input dimensionality for Brain Machine Interfaces (BMIs).
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Pages | 3062-3067 |
| Number of pages | 6 |
| DOIs | |
| State | Published - Dec 1 2007 |
| Externally published | Yes |
| Event | 2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: Aug 12 2007 → Aug 17 2007 |
Other
| Other | 2007 International Joint Conference on Neural Networks, IJCNN 2007 |
|---|---|
| Country | United States |
| City | Orlando, FL |
| Period | 8/12/07 → 8/17/07 |
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ASJC Scopus subject areas
- Software
Cite this
Hierarchal decomposition of neural data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI. / Darmanjian, Shalom; Paiva, Antonio; Principe, Jose; Sanchez, Justin C.
IEEE International Conference on Neural Networks - Conference Proceedings. 2007. p. 3062-3067 4371449.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hierarchal decomposition of neural data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI
AU - Darmanjian, Shalom
AU - Paiva, Antonio
AU - Principe, Jose
AU - Sanchez, Justin C.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - In this paper, we propose a simple algorithm that takes multidimensional neural input data and decomposes the joint likelihood into marginals using Boosted Mixtures of Hidden Markov Chains (BM-HMM). The algorithm applies techniques from boosting to create hierarchal dependencies between these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is weighted and incorporated into an ensemble decision. Our results show that this algorithm is very simple to train and computationally efficient, while also providing the ability to reduce the input dimensionality for Brain Machine Interfaces (BMIs).
AB - In this paper, we propose a simple algorithm that takes multidimensional neural input data and decomposes the joint likelihood into marginals using Boosted Mixtures of Hidden Markov Chains (BM-HMM). The algorithm applies techniques from boosting to create hierarchal dependencies between these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is weighted and incorporated into an ensemble decision. Our results show that this algorithm is very simple to train and computationally efficient, while also providing the ability to reduce the input dimensionality for Brain Machine Interfaces (BMIs).
UR - http://www.scopus.com/inward/record.url?scp=51749092420&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51749092420&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2007.4371449
DO - 10.1109/IJCNN.2007.4371449
M3 - Conference contribution
AN - SCOPUS:51749092420
SN - 142441380X
SN - 9781424413805
SP - 3062
EP - 3067
BT - IEEE International Conference on Neural Networks - Conference Proceedings
ER -