Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A standard method to obtain stochastic models for symbolic time series is to train state-emitting hidden Markov models (SE-HMMs) with the Baum-lch algorithm. Based on observable operator models (OOMs), in the last few months a number of novel learning algorithms for similar purposes have been developed: (1,2) two versions of an "efficiency sharpening" (ES) algorithm, which iteratively improves the statistical efficiency of a sequence of OOM estimators, (3) a constrained gradient descent ML estimator for transition-emitting HMMs (TE-HMMs).  give an overview on these algorithms and compare them with SE-HMM/EM learning on synthetic and real-life data.
['Herbert Jaeger', 'Mingjie Zhao', 'Andreas Kolling']
I am writing a research paper on a idea Efficient Estimation of OOMs
[{"from": "human", "value": "I am writing a research paper on a idea Efficient Estimation of OOMs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A standard method to obtain stochastic models for symbolic time series is to train state-emitting hidden Markov models (SE-HMMs) with the Baum-lch algorithm. Based on observable operator models (OOMs), in the last few months a number of novel learning algorithms for similar purposes have been developed: (1,2) two versions of an \"efficiency sharpening\" (ES) algorithm, which iteratively improves the statistical efficiency of a sequence of OOM estimators, (3) a constrained gradient descent ML estimator for transition-emitting HMMs (TE-HMMs).  give an overview on these algorithms and compare them with SE-HMM/EM learning on synthetic and real-life data."}]