Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a study about the possibility of implementing approximations to the Neyman–Pearson detector with C-Support Vector Machines and 2C-Support Vector Machines. It is based on obtaining the functions these learning machines approximate to after training to minimize the empirical risk, and on the possible implementation of the Neyman–Pearson detector with these approximated functions. The function approximated by a C-Support Vector Machine after perfect training is a binary function, with only two possible outputs. When the output of the C-Support Vector Machine is compared to a threshold, whose value is the intermediate beten the possible outputs, an implementation of the Maximum-A-Posteriori classifier is obtained. On the other hand, the function approximated by a 2C-Support Vector Machine after perfect training is also a binary function, but this machine implements the Neyman–Pearson detector for a fixed probability of false alarm and probability of detection pair, that can be selected with the parameter  γ  which controls the costs of the error function. Some experiments about radar detection have been carried out, in order to confirm the theoretical results. The results of these experiments allow us to confirm that the 2C-Support Vector Machine can implement very good approximations to the Neyman–Pearson detector.
['David de la Mata-Moya', 'Maria P. Jarabo-Amores', 'Jaime Nicolás', 'Manuel Rosa-Zurera']
I am writing a research paper on a idea Approximating the Neyman–Pearson detector with 2C-SVMs. I am writing a research paper on a idea Application to radar detection
[{"from": "human", "value": "I am writing a research paper on a idea Approximating the Neyman\u2013Pearson detector with 2C-SVMs. I am writing a research paper on a idea Application to radar detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a study about the possibility of implementing approximations to the Neyman\u2013Pearson detector with C-Support Vector Machines and 2C-Support Vector Machines. It is based on obtaining the functions these learning machines approximate to after training to minimize the empirical risk, and on the possible implementation of the Neyman\u2013Pearson detector with these approximated functions. The function approximated by a C-Support Vector Machine after perfect training is a binary function, with only two possible outputs. When the output of the C-Support Vector Machine is compared to a threshold, whose value is the intermediate beten the possible outputs, an implementation of the Maximum-A-Posteriori classifier is obtained. On the other hand, the function approximated by a 2C-Support Vector Machine after perfect training is also a binary function, but this machine implements the Neyman\u2013Pearson detector for a fixed probability of false alarm and probability of detection pair, that can be selected with the parameter  \u03b3  which controls the costs of the error function. Some experiments about radar detection have been carried out, in order to confirm the theoretical results. The results of these experiments allow us to confirm that the 2C-Support Vector Machine can implement very good approximations to the Neyman\u2013Pearson detector."}]