Title: Kernel Methods in non-parametric statistical learning
时间：6月14日， 下午3：00-5：00
地点：教三 338
Abstract: In this talk we first present our work on kernel methods for multi-modal image registration and non-parametric image segmentation. The main idea of these methods is using Renyi||s statistical dependence measure as an alternate similarity measure to mutual information for multi-modal images. This can be viewed to find the nonlinear feature maps such that the correlation coefficient of the multi-modal images in the feature space is maximized. By using the theory of RKHS these nonlinear maps can be obtained by optimizing a finite number of parameters. Inspired by these results, we will further discuss if we could combine the flexibility of kernel methods with the structural and scalable properties of deep neural networks to improve the learning ability of both methods.
Yunmei Chen教授，美国佛罗里达大学终身教授。陈韵梅教授主要致力于数学和图像科学这一交叉学科的研究。研究课题不仅包括图像分析中数学模型的建立与数值方法的发展，而且对其潜在的数学理论进行了进一步的探索。陈韵梅教授被公认为偏微分方程与图像处理领域内的世界级科学家，在国际上具有崇高的学术地位。
邀请人：刘华锋
