Abstract: A system and method for rank estimation of electromagnetic emitters is provided. One exemplary feature of the system and method includes the use of a Fixed Sigma Gaussian Mixture Model (FSGMM) to determine a rank estimation of electromagnetic emitters. Another exemplary feature of the system and method includes the use of a Gaussian Mixture Model (GMM) clustering approach in conjunction with an Akaike Criterion Information (AIC) to determine a number of clusters and associated statistics of emitters.

Abstract: A system and method for rank estimation of electromagnetic emitters is provided. One exemplary feature of the system and method includes the use of a Fixed Sigma Gaussian Mixture Model (FSGMM) to determine a rank estimation of electromagnetic emitters. Another exemplary feature of the system and method includes the use of a Gaussian Mixture Model (GMM) clustering approach in conjunction with an Akaike Criterion Information (AIC) to determine a number of clusters and associated statistics of emitters.

Abstract: A boosting—based method and system for fusing a set of classifiers that performs classification using weak learners trained on different views of the training data. The final ensemble contains learners that are trained on examples sampled with a shared sampling distribution. The combination weights for the final weighting rule are obtained at each iteration based on the lowest training error among the views. Weights are updated in each iteration based on the lowest training error among all views at that iteration to form the shared sampling distribution used at the next iteration. In each iteration, a weak learner is selected from the pool of weak learners trained on disjoint views based on the lowest training error among all views, resulting in a lower training and generalization error bound of the final hypothesis.

Abstract: A boosting—based method and system for fusing a set of classifiers that performs classification using weak learners trained on different views of the training data. The final ensemble contains learners that are trained on examples sampled with a shared sampling distribution. The combination weights for the final weighting rule are obtained at each iteration based on the lowest training error among the views. Weights are updated in each iteration based on the lowest training error among all views at that iteration to form the shared sampling distribution used at the next iteration. In each iteration, a weak learner is selected from the pool of weak learners trained on disjoint views based on the lowest training error among all views, resulting in a lower training and generalization error bound of the final hypothesis.