In order to extend common incremental learning algorithms into a parallel computation setting, an incremental learning algorithm with multiple support vector machine classifiers is proposed.
为了将一般增量学习算法扩展到并行计算环境中，提出一种基于多支持向量机分类器的增量学习算法。
This paper deals with the multivariate vector analysis of quality data by geometric transformation, rotating coordinates and computation of the principal component of initial variables.
本文讨论多元质量数据的矢量分析方法，几何变换，坐标旋转和初始变量的主分量计算不同变量的主分量值。
Meanwhile, two numerical computation object(including class Matrix Vector) are defined for the convenience of the programming.
另外，为了方便程序的实现，还引入了数值计算类，包括矩阵类、向量类。
The cost of the training of support vector machine is too much, if we use thousands of support vectors directly, the computation time would be too long.
支持向量机的训练代价太大，如果直接把成千上万个特征向量直接用作训练，运算时间难以忍受。
The vector surface integral equation can't be applied to numerical computation directly because its integrands possess the high order singularity.
矢量的面积分方程因其被积函数具有高阶奇异性，不能直接应用于数值计算。
In this paper a new choice of measurement vector and the corresponding computation of measurement probability are presented to reduce the quantization error.
本文提出另一种选择量测向量和计算量测概率的方法，创造条件减小量化误差。
For example, it costs more for storage and the distance computation is quite complex. With the number of images grows, it will be unsuitable for vector based image feature to stay in memory.
当图像数量增长到一定数量后，基于浮点矢量形式表示的图像特征就不适合放置在内存中，欧氏距离的计算也将造成较大的时间开销。
Even to bunch computation this vector algorithm will decrease assistant operation such as exchanging order.
该向量算法即使在串行机上实现，也减少了调序这一辅助操作。
The computation of vector quantization which is mainly depending on the search of the nearest codeword is the main barrier of practicalization of vector quantization.
矢量量化的计算量主要在于搜索最近的码字，这也是矢量量化实用化的主要障碍。
Methods Several variable points were picked out of the computation of the correspondence analysis, and were concluded from their vector velues.
方法将影响降维效果的个别变量点作为特例另行给出结论，不参与向量空间的特征描述。
On the complicated or implicit limit state functions in the reliability problems, a response surface method for reliability computation based on Support Vector Regression (SVR) is presented.
针对可靠度计算问题中极限状态函数比较复杂或为隐式的情况，提出了一种基于支持向量回归的响应面可靠度计算方法。
The main purpose of this research is on the computation task and the object is to analysis the accuracy of baseline vector from GPS observations.
本文通过实验全面的研究了影响中短距离基线解算精度的因素。
The main purpose of this research is on the computation task and the object is to analysis the accuracy of baseline vector from GPS observations.
本文通过实验全面的研究了影响中短距离基线解算精度的因素。
应用推荐