The researchers developed a method to improve pattern recognition by estimating the prior probabilities of different classes without prior knowledge. They used unidentified input patterns to create a better decision rule that minimizes misclassification. This method adapts while in use and can achieve misclassification probabilities close to the optimal Bayes rule. The researchers demonstrated the effectiveness of their approach through simulations with different types of distributions.