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The Improved Localized Generalization Error Model and Its Applications to Feature Selection for RBFNN. 报告人：马艳东. 指导教师：王熙照教授. 目录. 阿 Wing 的 L-GEM 基于范数的模型 Old model VS New model Algorithm for RBFNN Feature selection Simulation Conclusion Future Work. 阿 Wing 的 L-GEM.
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The Improved Localized Generalization Error Model and Its Applications to Feature Selection for RBFNN 报告人：马艳东 指导教师：王熙照教授
目录 • 阿Wing的L-GEM • 基于范数的模型 • Old model VS New model • Algorithm for RBFNN Feature selection • Simulation • Conclusion • Future Work
阿Wing的L-GEM Wing et al. ignore the unseen samples located far from the training samples and compute the generalization error within the Q-neighborhood.
Algorithm for RBFNN Feature selection based on • Step1. Initialize the IFS to be the full set of features; • Step2. Train the classifier using the dataset with features in IFS; • Step3. For each i: • Compute the for the classifier trained in Step2 without the i-th feature; • Step4. Delete the z-th feature from IFS if the for the z-th feature is the smallest among all choices; • Step5. If the stopping criterion is not satisfied, go to Step2.
Conclusion • NL-GEM becomes simpler and easier to derivate, understand and realize while the performance is satisfying . • But the time complexity is not low
Future Work • to do more experiments to test the validity of this model • to make use of MSE to realize the model
亟待解决的问题 • to improve the performance of time complexity • 方法一：另起炉灶，重新推导模型 • 方法二：用其他方法计算 • (1)按范数计算。 • 两种最常用的方法，Mente-Calro和网格法在time complexity上都是不太好； • (2)统计的方法。 • 的 不好去掉。
