SVM has been applied to many fields such as pattern recognition, data mining, modeling and control of nonlinear system due to good generalization ability and globally optimal performance.
SVM由于其良好的泛化能力和全局最优性能，在模式识别、数据挖掘、非线性系统建模和控制等领域中展现出广泛的应用前景。
Since multiple classifier systems(MCS) can improve the performance of classification, the technique has been widely used in various fields of pattern recognition.
多分类器组合方法可以在一定程度上弥补单个分类器的不足，提高分类性能，因此，它在模式识别领域得到广泛的应用。
In addition, the method of fuzzy pattern recognition was approached which made the performance evaluation of relay and its design to be realized.
同时，探讨了模糊模型的识别方法，从而实现了对继电器产品及其设计方案的性能评价。
Feature selection is an important issue in the fields of machine learning and pattern recognition. The effectiveness of feature directly affects the design and performance of the classifier.
特征选择问题是机器学习和模式识别中的一个重要问题，特征的优劣直接影响分类器的设计和性能。
Since multiple classifier systems can to some extent improve the performance of classification, the technique has been widely used in various fields of pattern recognition.
多分类器系统能够在一定程度上弥补单个分类器的缺陷，因此它在模式识别中得到了广泛的应用。
Chaos synchronization has very important applications in security communication, optimization of nonlinear system performance, modeling brain activity and pattern recognition.
混沌同步在保密通信、非线性系统性能优化、大脑行为建模以及模式识别等领域有着极为重要的应用。
Due to its complete theoretical basis as well as excellent performance, SVM has become a hotspot in the area of pattern recognition.
由于具有完备的理论基础和良好的性能，支持向量机已经成为模式识别的一个研究热点。
Support Vector machine is a new machine-learning method and has its unique advantages in pattern recognition because of outstanding learning performance and good capabilities in generalization.
支持向量机是一种全新的机器学习方法，其出色的学习性能和泛化能力强等方面的优势，在模式识别领域中有其独到的优越性。
The classifier design is a key step for pattern recognition systems, which can effect performance of system.
分类器设计是模式识别系统中的关键步骤之一，它直接影响到系统的分类能力。
Data mining techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization.
数据挖掘技术起源于从统计方法，模式识别，数据库，人工智能，高性能和并行计算和可视化。
Data mining techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization.
数据挖掘技术起源于从统计方法，模式识别，数据库，人工智能，高性能和并行计算和可视化。
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