Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of estimating one of the parameters of a marked point process, namely the tradeoff parameter beten the data and prior energy terms defining the probability density of the process. In previous work, the Stochastic Expectation-Maximization (SEM) algorithm was used. Hover, SEM is ll known for having bad convergence properties, which might also slow down the estimation time. Therefore, in this work,  consider an alternative to SEM: the Stochastic Approximation EM algorithm, which makes an efficient use of all the data simulated.  compare both approaches on high resolution satellite images where the objective is to detect boats in a harbor.
['Aurélie Boisbunon', 'Josiane Zerubia']
I am writing a research paper on a idea Estimation of the Weight Parameter with SAEM for Marked Point Processes Applied to Object Detection
[{"from": "human", "value": "I am writing a research paper on a idea Estimation of the Weight Parameter with SAEM for Marked Point Processes Applied to Object Detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of estimating one of the parameters of a marked point process, namely the tradeoff parameter beten the data and prior energy terms defining the probability density of the process. In previous work, the Stochastic Expectation-Maximization (SEM) algorithm was used. Hover, SEM is ll known for having bad convergence properties, which might also slow down the estimation time. Therefore, in this work,  consider an alternative to SEM: the Stochastic Approximation EM algorithm, which makes an efficient use of all the data simulated.  compare both approaches on high resolution satellite images where the objective is to detect boats in a harbor."}]