Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The basic idea of boosting is to increase the pattern recognition accuracy by combining classifiers which have been derived from differently ighted versions of the original training data. It has been verified in practical experiments that the resulting classification performance can be improved by increasing the ights of misclassified training samples. Hover in statistical pattern recognition, the ighted data may influence the form of the estimated conditional distributions and therefore the theoretically achievable classification error could increase.  prove that in case of maximum-likelihood estimation the ighting of discrete data vectors is asymptotically equivalent to multiplication of the estimated discrete conditional distributions by a positive bounded function. Consequently, the Bayesian decision-making is shown to be asymptotically invariant with respect to arbitrary ighting of data provided that (a) the ighting function is defined identically for all classes and (b) the prior probabilities are properly modified.
['Jir ´ i Grim', 'Pavel Pudil', 'Petr Somol']
I am writing a research paper on a idea Boosting in probabilistic neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Boosting in probabilistic neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The basic idea of boosting is to increase the pattern recognition accuracy by combining classifiers which have been derived from differently ighted versions of the original training data. It has been verified in practical experiments that the resulting classification performance can be improved by increasing the ights of misclassified training samples. Hover in statistical pattern recognition, the ighted data may influence the form of the estimated conditional distributions and therefore the theoretically achievable classification error could increase.  prove that in case of maximum-likelihood estimation the ighting of discrete data vectors is asymptotically equivalent to multiplication of the estimated discrete conditional distributions by a positive bounded function. Consequently, the Bayesian decision-making is shown to be asymptotically invariant with respect to arbitrary ighting of data provided that (a) the ighting function is defined identically for all classes and (b) the prior probabilities are properly modified."}]