Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The approximated classifier is expected to have worse performance, here measured by the probability of correct classification.  present an analysis valid in general, and easily computable formulas for estimating the degradation in probability of correct classification when compared to the optimal classifier. An example of an approximation is the Naive Bayes classifier.  show that the performance of the Naive Bayes depends on the degree of functional dependence beten the features and labels.  provide a sufficient condition for zero loss of performance, too.
['Magnus Ekdahl', 'Timo Koski']
I am writing a research paper on a idea Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
[{"from": "human", "value": "I am writing a research paper on a idea Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The approximated classifier is expected to have worse performance, here measured by the probability of correct classification.  present an analysis valid in general, and easily computable formulas for estimating the degradation in probability of correct classification when compared to the optimal classifier. An example of an approximation is the Naive Bayes classifier.  show that the performance of the Naive Bayes depends on the degree of functional dependence beten the features and labels.  provide a sufficient condition for zero loss of performance, too."}]