Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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 beten these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is ighted 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).
['Shalom Darmanjian', 'Antonio R. C. Paiva', 'Jose C. Principe', 'Justin C. Sanchez']
I am writing a research paper on a idea Hierarchal Decomposition of Neural Data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI
[{"from": "human", "value": "I am writing a research paper on a idea Hierarchal Decomposition of Neural Data using Boosted Mixtures of Hidden Markov Chains and its application to a BMI"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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 beten these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is ighted 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)."}]