Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In supervised learning problems, global and local learning algorithms are used. In contrast to global learning algorithms, the prediction of a local learning algorithm in a testing point is only based on training data which are close to the testing point. Every global algorithm such as support vector machines (SVM) can be localized in the following way: in every testing point, the (global) learning algorithm is not applied to the whole training data but only to the k nearest neighbors (kNN) of the testing point. In case of support vector machines, the success of such mixtures of SVM and kNN (called SVM-KNN) has been shown in extensive simulation studies and also for real data sets but only little has been known on theoretical properties so far. In the present article, it is shown how a large class of regularized kernel methods (including SVM) can be localized in order to get a universally consistent learning algorithm.
['Robert Hable']
I am writing a research paper on a idea Universal consistency of localized versions of regularized kernel methods
[{"from": "human", "value": "I am writing a research paper on a idea Universal consistency of localized versions of regularized kernel methods"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In supervised learning problems, global and local learning algorithms are used. In contrast to global learning algorithms, the prediction of a local learning algorithm in a testing point is only based on training data which are close to the testing point. Every global algorithm such as support vector machines (SVM) can be localized in the following way: in every testing point, the (global) learning algorithm is not applied to the whole training data but only to the k nearest neighbors (kNN) of the testing point. In case of support vector machines, the success of such mixtures of SVM and kNN (called SVM-KNN) has been shown in extensive simulation studies and also for real data sets but only little has been known on theoretical properties so far. In the present article, it is shown how a large class of regularized kernel methods (including SVM) can be localized in order to get a universally consistent learning algorithm."}]