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Deriving Probabilistic SVM Kernels From Flexible Statistical Mixture Models and its Application to Retinal Images Classification

机译：从灵活的统计混合模型推导概率SVM核及其在视网膜图像分类中的应用

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This paper aims to propose a robust hybrid probabilistic learning approach that combines appropriately the advantages of both the generative and discriminative models for the challenging problem of diabetic retinopathy classification in retinal images. We build new probabilistic kernels based on information divergences and Fisher score from the mixture of scaled Dirichlet distributions for support vector machines (SVMs). We also investigate the incorporation of a minimum description length criterion into the learning model to deal with the common problems of determining suitable components and also selecting the best model that describes the dataset. The developed hybrid model is introduced in this paper as an effective SVM kernel able to incorporate prior knowledge about the nature of data involved in the problem at hand and, therefore, permits a good data discrimination. Our approach has been shown to be a better alternative to other methods, which is able to describe the intrinsic nature of datasets and to be of a significant value in a variety of applications involving data classification. We demonstrate the flexibility and the merits of the proposed framework for the problem of diabetic retinopathy detection in eye images.
机译：本文旨在提出一种鲁棒的混合概率学习方法，该方法适当地结合了生成模型和判别模型的优点，以解决视网膜图像中糖尿病性视网膜病变分类的挑战性问题。我们基于支持向量机（SVM）的按比例缩放的Dirichlet分布的混合，基于信息差异和Fisher分数构建新的概率内核。我们还研究了将最小描述长度标准合并到学习模型中，以解决确定合适组件并选择描述数据集的最佳模型的常见问题。本文将开发的混合模型作为有效的SVM内核进行介绍，该内核能够合并有关问题中涉及的数据性质的先验知识，因此可以很好地区分数据。我们的方法已被证明是其他方法的更好替代方法，该方法能够描述数据集的固有性质，并且在涉及数据分类的各种应用中具有重要价值。我们证明了在眼图像中糖尿病性视网膜病变检测问题的拟议框架的灵活性和优点。

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