The maximum entropy principle advocates to evaluate events' probabilities using a distribution that maximizes entropy among those that satisfy certain expectations' constraints. Such principle can be generalized for arbitrary decision problems where it corresponds to minimax approaches. This paper establishes a framework for supervised classification based on the generalized maximum entropy principle that leads to minimax risk classifiers (MRCs). We develop learning techniques that determine MRCs for general entropy functions and provide performance guarantees by means of convex optimization. In addition, we describe the relationship of the presented techniques with existing classification methods, and quantify MRCs performance in comparison with the proposed bounds and conventional methods.

翻译：最大限度的昆虫原则主张利用在符合某些期望的限制因素中最大限度地增加酶的分布来评估事件的概率; 这项原则可以适用于任意决定问题,只要符合小型方法; 本文根据普遍的最高酶原则,为监督分类建立一个框架,以导致小型危险分类者(MRCs); 我们开发学习技术,确定一般酶功能的MRCs,并通过onvex优化提供性能保障; 此外,我们描述介绍的技术与现有分类方法的关系,并将MRCs的表现与拟议的界限和常规方法相比较,量化。