Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a corrective bandwidth learning algorithm for Kernel Density Estimation (KDE)-based classifiers. The objective of the corrective bandwidth learning algorithm is to minimize the expected error-rate. It utilizes a gradient descent technique to obtain the appropriate bandwidths. The proposed classifier is called the "Empirical Mixture Model" (EMM) classifier. Experiments re conducted on a set of multivariate multi-class classification problems with various data sizes. The proposed classifier has an error-rate closer to the true model compared to conventional KDE-based classifiers for both small and large data sizes. Additional experiments on standard machine learning datasets shod that the proposed bandwidth learning algorithm performed very ll in gen-eral.
['Dwi Sianto Mansjur', 'Biing Hwang Juang']
I am writing a research paper on a idea Improving Kernel Density Classifier Using Corrective Bandwidth Learning with Smooth Error Loss Function
[{"from": "human", "value": "I am writing a research paper on a idea Improving Kernel Density Classifier Using Corrective Bandwidth Learning with Smooth Error Loss Function"}, {"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 corrective bandwidth learning algorithm for Kernel Density Estimation (KDE)-based classifiers. The objective of the corrective bandwidth learning algorithm is to minimize the expected error-rate. It utilizes a gradient descent technique to obtain the appropriate bandwidths. The proposed classifier is called the \"Empirical Mixture Model\" (EMM) classifier. Experiments re conducted on a set of multivariate multi-class classification problems with various data sizes. The proposed classifier has an error-rate closer to the true model compared to conventional KDE-based classifiers for both small and large data sizes. Additional experiments on standard machine learning datasets shod that the proposed bandwidth learning algorithm performed very ll in gen-eral."}]