核口袋算法的特点是用简单的迭代过程和核函数来实现非线性分类器的设计。
Its advantage is to implement a nonlinear classifier using a simply iterative procedure and kernel functions.
然后，通过设计的模糊推理规则进行模式的分类，这种分段线性分类器的设计提高了算法线性分类的能力。
Then patterns are categorized by the designed fuzzy inference regulation. The design of this piecewise linear classifier enhances the ability of linear classification of the algorithm.
使用线性分类器进行分类，并用“留一法”统计结果，正常人和早期DR病例的分类错误率为21.35%。
The classification error rate for normal and early stage DR samples reached 21.35% using a linear classifier and the leave-one-out method.
模拟结果显示这三类分类器适合应用于雷达目标的分类识别，且具有很高的识别率，是一类简单、高效的非线性分类器。
Experimental results show that these three classifiers can get high recognition rate. they are simple, efficient, nonlinear and suitable for RTR. 2.
在分类器设计环节，比较五种核非线性分类器，并根据宽带极化雷达目标散射数据的特点，使用融合分类的方法对目标进行分类。
In classification stage, five kernel-based classifications are used and compared, and fusion methods are designed for wide-band polarimetric radar target classification.
然后根据裂缝的线性特征，设计分类器，得到包含裂缝及非裂缝的线性目标图。
At last, according to the line character we designed the classifier to obtain the object image including linear distress and nonlinear ones.
本文实现了基于分类器判决可靠度估计的最优线性集成方法。
This text realizes Optimal Linear Combination method basing on recognition confidence.
为了实现目标的快速检测，提出了一种新的基于拉格朗日支持向量机(L -SVM)的线性级联式分类器的构造方法。
To detect objects quickly, a new method is presented to construct a cascade of linear classifiers with L-SVM (Lagrangian Support Vector Machine, L-SVM).
感知器是一种有用的神经网络模型，可以对线性可分的模式进行正确分类。
Perceptron is a kind of useful neural network model and can classify the classification of the detachable linearity correctly.
结果表明，以CP神经网络构筑的故障模式识别器有很强的非线性映射能力，可对机械设备故障模式进行正确分类。
The result indicates that based on CP neural network, the fault pattern recognition system has strong nonlinear mapping ability, therefore it can be used to correctly classify the mechanical faults.
提出了神经网络控制器的分类以及非线性映射特性，讨论了神经网络控制器的特点。
This paper proposes the classification of the neural network controller architecture and dynamic neural network structure, and clarifies its nonlinear mapping ability.
而基于模糊规则的模式识别方法是一类可理解性好的非线性方法，但迄今为止还没有被应用于多分类器融合问题中。
As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion.
而基于模糊规则的模式识别方法是一类可理解性好的非线性方法，但迄今为止还没有被应用于多分类器融合问题中。
As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion.
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