To effectively identify driving mental fatigue states, a new method based on kernel learning algorithm is presented.
为了有效地评测人的驾驶精神疲劳状态，本文提出了一种基于核学习算法的精神疲劳分级方法。
Based on decision tree combined strategy and multiple kernel learning support vector machines, a new fault diagnosis method is proposed to improve the precision and speed of fighter fault diagnosis.
为了提高歼击机故障诊断的准确性与实时性，提出一种基于决策树型组合策略的多重核学习支持向量机诊断方法。
Those who are new to kernel development can work on items from this list, allowing them a chance to benefit the community while learning how to write kernel code on smaller projects.
那些新近从事内核开发的人开始时的工作可以选择列表中的条目，这样让他们可以通过小项目学习如何编写内核代码，同时有机会为社区做出贡献。
They USES Structural Risk Minimization and the kernel trick to solve the learning problems.
它使用结构风险最小化原则，运用核技巧，较好地解决了学习问题。
However, Every kernel has its advantages and disadvantages. Combining two or more kernels is one of efficient method for improving the ability of learning and generalization.
然而，每种核函数都有它的优势和不足，整合两个或多个核函数对于学习能力和泛化能力的提高是一个有效的途径。
Kernel function is the key technology of SVM, the choice of kernel will affect the learning ability and generalization ability of SVM.
核函数是SVM的关键技术，核函数的选择将影响着支持向量机的学习能力和泛化能力。
Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed.
最后讨论了离散尺度与小波核函数的构造，核函数选择与核参数学习。
The new modular kernel function not only has a good learning ability, but also applies the full information which the high-dimensional spectra remote sensing image provided.
新型的组合式核函数既具有较好的学习能力，又能充分应用高光谱图像所提供的信息。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
This paper introduces a fuzzy classification model based on the proposed fuzzy kernel hyperball perceptron(FKHP) learning method.
本文提出一种模糊核超球感知器（FKHP）学习方法，并介绍了一种基于FKHP这种学习方法的模糊分类模型。
Moreover, SVM can convert a nonlinear learning problem into a linear learning problem in order to reduce the algorithm complexity by using the kernel function concept.
又由于采用了核函数思想，使它将非线性问题转化为线性问题来解决，降低了算法的复杂度。
Moreover, SVMs can change a nonlinear learning problem in to a linear learning problem in order to reduce the algorithm complexity by using the kernel function idea.
又由于采用了核函数思想，使它把非线性问题转化为线性问题来解决，降低了算法的复杂度。
Considering the learning and extrapolating ability as well as the parameter optimizing time, linear kernel is determined to be used in SVM in the analysis of diesel engine exhaust emissions.
综合考虑SVM的学习能力、外推能力及寻优时间，决定选择线性核函数作为SVM在柴油机尾气分析中的核模型。
Kernel Canonical Correlation Analysis (KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features.
针对该问题，采用核典型相关分析方法进行原始特征的二次提取，得到简约而重要的二次特征。
Kernel Canonical Correlation Analysis (KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features.
针对该问题，采用核典型相关分析方法进行原始特征的二次提取，得到简约而重要的二次特征。
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