因为MASS向量函数要比对一个标准库函数的重复访问快很多(倍数接近30倍)，所以最后得到的性能改善效果将会是惊人的。
Since the MASS vector functions are much faster (by a factor of up to about 30) than a repeated call to a standard library function, the resulting performance improvement can be substantial.
向量处理器系统的性能很高，在20世纪80年代到90年代早期一度在HPC体系架构中占有统治地位，但是最近几年以来，集群变得更加流行了。
Vector processor systems deliver high performance and were the dominant HPC architecture in the 1980s and early 1990s, but clusters have become far more popular in recent years.
另外也有用于IBMPowerPC 970处理器的VMX向量扩展，可以提高向量化代码的性能。
There are also VMX vector extensions for the IBM PowerPC 970 processor that can increase the performance of vectorized code.
当权向量受到噪声的影响时，最小输出能量(MOE)检测器的性能将显著下降。
The performance of minimum output energy (MOE) detector will significantly degrade when the weight vector is affected by the noise.
传统向量空间模型在计算复杂度、查询性能、智能性方面存在种种缺陷。
There are disadvantages of traditional vector space model in computational complexity, query efficiency and intelligence.
首先采用了一种递归的估计条件众数算法来产生一组使得系统不稳定或性能不可接受的不确定参数向量样本。
First, a recursive algorithm estimating conditional mode was employed to generate a set of uncertain parameter vector samples which lead to instability or unacceptable performance of systems.
为更好的解决这一问题，论文提出一种基于预测性运动向量的菱形搜索方法，有效的提高了视频压缩的性能。
For resolving the problem better, this paper presents an improved diamond search algorithm based on motion vector prediction, and it can efficiently improve the performance of video compression.
仿真研究表明，SVM具有优良的逆模型辨识能力，基于模糊控制补偿的支持向量机逆控制系统的动态性能好、跟踪精度高、鲁棒稳定性强。
Simulations demonstrate that SVM has good nonlinear approximation capability for inverse model, and the proposed control system has good dynamic and static performances as well as good robustness.
与统计分析和神经网络相比，基于结构风险最小的支持向量机有更好的分类性能。
Compared with multivariate statistics and artificial neural networks, support vector machine based on structure risk minimization has better classification performance.
回归型支持向量机方法SVR具有很好的学习性能。
Support Vector Machine for regression (SVR) has shown very good learning performance.
为了提高支持向量机(SVM)的识别性能，提出了在常用内核的基础上构造一个组合内核函数，然后用拟牛顿算法对其超参数进行优化的方法。
To improve the performance of support vector machines (SVM), a hybrid kernel is constructed from the existing common kernels, and the hyper-parameters are optimized by using a quasi-Newton method.
因其独特的制造技术，MEMS检波器具有超低噪声，大动态范围和极高的向量保真度性能。
Since its unique manufacturing technology, the MEMS geophone has ultra-low noise, larger dynamic range and very high vectorial fidelity performances.
高级预测模式可与无限制运动向量模式一起使用以提高预测性能及降低方块效应。
Combined with Unrestricted Motion Vector mode advanced prediction mode can be used to improve the prediction performance and reduce the "blocking" effects.
为了进一步提升多分辨率信号逼近算法(msa)的逼近性能，提出了一种基于支持向量机(SVM)的信号多分辨率逼近算法(SVM - msa)。
To further improve the approximation performance of multiresolution signal approximation (MSA) algorithm, a new MSA algorithm based on support vector machine (SVM), named SVM-MSA is proposed.
实验结果表明：简单的SLM-CIR模型的性能要优于简单的向量空间模型和概率模型。
The experimental result shows that the performance of simple SLM-CIR model should be superior to simple vector space models and probability models.
该方法可降低数据空间维数和支持向量机处理过程的复杂度，但不会降低分类和预测性能。
The method can reduce the dimensions of the data set and the complexity of the model of SVMs, and doesn't affect its classification and prediction performance.
当权向量受到噪声的影响时，最小输出能量(MOE)检测器的性能将显著下降。
The performance of the minimum output energy (MOE) detector will degrade when it is affected by the noise in the weight vector.
在实际工业数据上进行的实验结果表明，支持向量机模型对丙酮纯度具有良好的预测效果，性能优于反向传播神经网络和径向基网络模型。
The experimental results on the real industrial data demonstrate that the model based on SVM achieves good performance and has less prediction errors than those of BPNN and RBFNN models.
支持向量机(SVM)是一种基于结构风险最小化原理，具有很好推广性能的学习算法。
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and high generalization ability.
基于统计学习理论的支持向量机是一类新型的机器学习算法，由于它出色的学习性能，该技术已经成为当前学术界的研究热点。
The support vector machine based on statistical learning is a new type of machine learning algorithm, which has become the hot spot of academic study because of its excellent learning performance.
实验表明支持向量机提高了过滤性能。
Experimental results showed that support vector machine could improve the filtering performance.
在回归支持向量机的建模中，参数调节问题一直是影响模型性能的重要因素之一。
Parameter tuning of Support Vector Regression (SVR) has been a critical task to develop a SVR model with good generalization performance.
由于其出色的学习和推广性能，支持向量机已经被应用到许多方面，例如：文本分类、人脸识别、指纹识别等等。
Because of its excellent learning ability, SVM has been applied to such fields as Text Classification, Face Recognition, and Figure Recognition.
提出了一种基于粗糙集(RS)和支持向量机(SVM)的目标对象的性能分类方法，该方法将RS和SVM结合在一起对性能进行分类。
A method of object's performance classification based on Rough Set (RS) and Support Vector Machines (SVM) was proposed and it classifies the object's performance by composing the RS and SVM.
测试结果表明，新的负载向量及新的阈值确定方法缩短了任务的响应时间，提高了集群的性能。
Experimental results show that the new load index and the new way to consider threshold decrease the executing time of tasks and improve the cluster's performance.
为了获得所需要的高性能，有必要采用最快和最可靠的硬件并应用向量处理与并行处理等新技术的革新过程。
To achieve the required level of high performance it is necessary to utilize the fastest and most reliable hardware and apply innovative procedures from vector and parallel processing techniques.
为了使数字水印综合性能更好，根据图像邻域像素之间具有很强的相关性这一特点，提出了一种基于支持向量机的图像水印算法。
Considering the coherence among neighborhood pixels in an image, a kind of spatial domain watermarking scheme based on support vector machine is proposed.
支持向量机是一种基于结构风险最小化原理的学习技术，也是一种新的具有很好泛化性能的回归方法。
Support vector machine is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
近年来，支持向量机算法被广泛地用于检索函数(排位函数)学习问题上且应用性能卓越。
In recent years, support vector machine algorithm is widely used on the issue of learning of retrieval function (ranking function) and performs excellently.
近年来，支持向量机算法被广泛地用于检索函数(排位函数)学习问题上且应用性能卓越。
In recent years, support vector machine algorithm is widely used on the issue of learning of retrieval function (ranking function) and performs excellently.
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