SEI: Knowledge-Based Data Classification, Approximation and Optimization

海量数据集出现在各种类型的环境中，从高度科学的环境到无处不在的互联网。要理解这些海量数据需要复杂的计算机科学技术，如数据分类、近似和优化。所有这些技术都可以通过有效地利用通常容易获得的先验知识而得到实质性的改进。例如，可以利用医生的经验获得针对各种类型的重要问题的改进分类器，例如医疗诊断和预后。由于最强大的最先进的分类器是基于支持向量机的，而支持向量机又被表述为约束或无约束优化问题，其目的是将先验知识纳入各种基于优化的应用中，例如分类和近似问题，以及优化理论本身。在很大程度上，这项提议的动机是研究人员与肿瘤学家、外科医生和医学物理学家的广泛合作，以及研究人员希望通过将其纳入可计算但严格的模型来充分利用这些从业者的专业知识。所提出的工作的智力价值在于使用严格的理论和问题分析技术，将特定领域的信息纳入一般优化问题。该研究将首先将知识整合到线性或非线性支持向量机分类器中，并表明通过在原始问题上附加附加约束，这种整合是可能的。初步测试表明分类器的准确性有所提高。其次，将先验知识引入近似问题。因此，除了通常用于生成未知函数近似值的给定离散数据外，还考虑了先验知识。最后，将先验知识纳入一般约束或无约束优化问题，其中先验知识由新的约束组成，这些约束将施加于目标函数在各个区域上的行为。这些新技术的通用性将促进来自不同来源的信息的集成，因为该理论允许同时包含多组先验信息。放射治疗计划问题的具体应用将确保计算机科学的进步在一个特定的问题领域是明显有用的。优化、建模和计算技术将促进癌症诊断和预后、化疗和其他治疗方案的进步。基于知识的方法包含了广泛的重要分类和近似问题，在科学和工程中具有广泛的适用性。这项工作还将提高数据挖掘技术在其他领域的地位，如外科、药理学和医学研究，通过展示如何利用这些方法将先验知识纳入规划和设计问题，并在许多临床环境中提高交付效率和治疗效果。通过将几名计算机科学和工程专业学生的教育与拟议的工作结合起来，将培训一批新的多学科研究人员，以确保技术进步应用于进一步的应用领域。

AbstractNSF-0511905Mangasarian, OlviMassive datasets occur in all types of settings ranging from the highly scientific to the ubiquitous internet. Making sense of this massive data requires sophisticated computer sciences techniques such as data classification, approximation and optimization. All of these techniques can be improved substantially by making effective use of prior knowledge that is often readily available. For example doctors' experience can be utilized in obtaining improved classifiers for various types of important problems, such as medical diagnosis and prognosis. Since the most powerful state-of the-art classifiers are based on support vector machines, which in turn are formulated as constrained or unconstrained optimization problems, the aim is that prior knowledge be incorporated into various optimization-based applications such as classification and approximation problems as well into the theory of optimization itself. To a large degree, this proposal is motivated by the investigators' extensive collaborative work with oncologists, surgeons and medical physicists and the investigators' desire to make full use of the expertise of such practitioners by incorporating it into computable but rigorous models.The intellectual merit of the proposed work lies in the use of rigorous theory and problem analysis techniques that incorporate domain specific information into general optimization problems. The research will first incorporate knowledge into a linear or nonlinear support vector machine classifier and show that such incorporation is possible by appending additional constraints to the original problem. Preliminary tests indicate improvements in classifier accuracy. Secondly, prior knowledge will be introduced into approximation problems. Thus, in addition to given discrete data that is normally used to generate an approximation to an unknown function, prior knowledge is also taken into account. Finally, prior knowledge will be incorporated into general constrained or unconstrained optimization problems, wherein the prior knowledge consists of new constraints to be imposed on the behavior of the objective function on various regions. The generality of these new techniques will facilitate the integration of information from disparate sources, since the theory allows multiple sets of prior information to be included concurrently. Specific application to radiotherapy treatment planning problems will ensure the computer science advancements are demonstrably useful in a particular problem domain.The optimization, modeling, and computational techniques will provide a boost to advances in cancer diagnosis and prognosis, chemotherapy, and other treatment regimes. The knowledge-based approach encompasses a broad spectrum of important classification and approximation problems that have wide applicability in science and engineering. The work will also raise the profile of data mining techniques in other areas such as surgery, pharmacology, and medical research, by demonstrating how the methodologies can be utilized to incorporate prior knowledge into both planning and design issues, and improving both efficiency of delivery and effectiveness of treatment in many clinical settings. By coupling the education of several computer science and engineering students with the proposed work, a new group of multidisciplinary researchers will be trained that will ensure the technical advances are applied to further application domains.