Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  extend the PAC-Bayes theorem to the sample-compression setting where each classifier is represented by two independent sources of information: a  compression set  which consists of a small subset of the training data, and a  message string  of the additional information needed to obtain a classifier. The new bound is obtained by using a prior over a data-independent set of objects where each object gives a classifier only when the training data is provided. The new PAC-Bayes theorem states that a Gibbs classifier defined on a posterior over sample-compressed classifiers can have a smaller risk bound than any such (deterministic) sample-compressed classifier.
['Françcois Laviolette', 'Mario Marchand']
I am writing a research paper on a idea PAC-Bayes risk bounds for sample-compressed Gibbs classifiers
[{"from": "human", "value": "I am writing a research paper on a idea PAC-Bayes risk bounds for sample-compressed Gibbs classifiers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  extend the PAC-Bayes theorem to the sample-compression setting where each classifier is represented by two independent sources of information: a  compression set  which consists of a small subset of the training data, and a  message string  of the additional information needed to obtain a classifier. The new bound is obtained by using a prior over a data-independent set of objects where each object gives a classifier only when the training data is provided. The new PAC-Bayes theorem states that a Gibbs classifier defined on a posterior over sample-compressed classifiers can have a smaller risk bound than any such (deterministic) sample-compressed classifier."}]