SGER: New Approaches for Design of Nonlinear Discriminants

9527477 Prabhu 这项研究由 SGER（探索性研究小额资助）资助。该项目的目标是加强用于模式识别的计算机辅助非线性判别式的设计、开发和实现。该方法将采用代数几何和动态系统的技术。判别式设计使用特征向量数据库，并将特征空间划分为与不同类别模式相对应的不相交区域。根据该方法，将开发并实施算法。该算法的性能将通过天气预报和生产系统中需要快速模式识别和控制的一系列测试问题来衡量。 快速识别物体并对其采取行动，特别是在涉及自动装配和零件分类的情况下，是设计和实施有效的自动化生产系统的基本品质。该技术除了其非线性判别性质外，预计通常会在模式识别的速度和准确性方面产生一些改进。开发能够在很短的时间内高精度处理大量数据的快速算法对于在许多应用中大规模采用自动模式识别技术至关重要。这项研究的成果有可能推动这一关键领域的最新技术发展。

9527477 Prabhu This research is funded as an SGER (Small Grant for Exploratory Research). The objective of the project is to enhance the design, development, and implementation of a computer-aided nonlinear discriminant for pattern recognition. The approach will employ techniques from algebraic geometry and dynamic systems. Discriminant design uses a database of feature vectors and partitions the feature space into disjoint regions corresponding to different classes of patterns. Based on the methodology, an algorithm will be developed and implemented. The performance of the algorithm will be benchmarked through a series of tests problems in weather forecasting and production systems for which quick pattern recognition and control are required. To rapidly recognize and act on objects, especially in situations involving automatic assembly and part sortation, are essential qualities for the design and implementation of effective automated production systems. The technique, apart from its nonlinear discriminant nature, is expected to yield some improvement in both speed and accuracy in pattern recognition in general. The development of fast algorithms capable of processing large amounts of data in very short time and with high accuracy is essential for large scale adoption of automatic pattern recognition techniques in a host of applications. The outcome of this research has the potential to advance the state-of-the-art in this critical field.