You do not need, nor should you expect, an LMS to solve this problem.
你并不需要一整套的学习管理系统来解决你的问题，你也没必要期待它。
LMS algorithm is of lower complexity but poor convergence performance.
LMS算法复杂度低，易于实现，但是收敛性能较差。
IBM has announced that no new features will be added to the LMS code stream.
IBM已经宣布，将不会添加新的特性到LMS代码流中。
Architecture and design documentation for the LMS was created, specifically.
创建了LMS架构和设计文档，具体如下。
The training of the output layer is the supervised algorithm based on LMS.
输出层的训练是采用基于LMS的监督式数学模型。
This is because the LMS is lighter, has fewer features, and is simpler to scale.
这是因为LMS是轻量级的，具有的特性较少，并且使用更简单。
The number of LMS cases is too small for stratification for risk assessment.
由于LMS的病例太少，所以无法对其风险评估进行分级。
The effects of finite word length in LMS adaptive noise canceller are discussed.
本文讨论了LMS自适应噪声对消器的有限字长效应。
Specifically, these are learning management systems (LMS) and operating systems.
明确地说，这些应用是学习管理系统(LMS)和操作系统。
These circumstances directly influenced some of the architectural decisions for the LMS.
这些环境直接影响了LMS的某些架构决定。
Content with similar compliance standards can be readily deployed on the LMS platform.
在LMS平台上可以很容易的应用符合其它相似标准的内容。
In early 2004, LMS 1.0.3 was released in conjunction with Workplace Collaborative Learning 2.0.
在2004年年初，LMS1.0.3与WorkplaceCollaborative Learning 2.0一起发布。
Next came an assessment of which platform and which software packages would be used to run the LMS.
接下来评估哪个平台和那些软件包可以用于运行LMS。
This paper introduces a modified LMS algorithm and its application in adaptive noise cancellation.
介绍了一种改进的LMS自适应滤波算法及其在自适应噪声对消中的应用。
The normalized LMS (NLMS) algorithm is a current algorithm usually used in an adaptive noise canceller.
归一化lms (NLMS)算法是目前自适应噪声对消器中常用的一种算法。
Think about the following questions when deciding between Workplace Collaborative Learning 2.5 and LMS 1.0.5.
当要在WorkplaceCollaborative Learning 2.5和LMS 1.0.5之间作出选择时，请仔细考虑下列问题。
Based on a novel windowed overlapping adaptive filter, LMS algorithm with overlapping idea is introduced.
基于一种带窗重叠自适应滤波器，将重叠滤波思想引入LMS算法。
LMS 1.0.5 SP2 is currently on target for the second half of 2005, and additional service packs are planned in 2006.
LMS1.0.5 SP2准备在2005年的下半年发布，并计划在2006年发布附加服务包。
IBM quickly released four portlets to interface with LMS 1.0.2 in the background via the existing Web Services API.
IBM迅速发布了4个portlet，通过现有的WebServicesAPI在后台与LMS1.0.2连接。
Up to this point in the article, we've been explaining how LMS and Workplace Collaborative Learning are largely the same.
本文就这一点已经解释了LMS和WorkplaceCollaborative Learning在很大程度上是相同的。
If you expect your organization to be on a tight budget for the foreseeable future, then LMS could be an attractive choice.
如果期望您的企业在可预测的未来一段时间里的预算不是很高，那么LMS将是一个极具吸引力的选择。
This is because (as mentioned earlier in this article) Workplace Collaborative Learning is in fact the next release of LMS.
这是因为（正如本文中前面所提到的）WorkplaceCollaborative Learning实际上就是 LMS的下一版本。
Echo channel estimation and the tracking of channel using the transform domain LMS algorithm are introduced in this article.
提出了对回波信道的初估计并利用变换域LMS算法进行信道跟踪的一种方案。
Workplace Collaborative Learning 2.5 and LMS 1.0.5 use the same base code and thus are virtually identical products in many ways.
WorkplaceCollaborativeLearning 2.5和LMS1.0.5使用相同的代码，因此在许多方面它们实质上是相同的产品。
Speech noise reduction algorithm, noise cancellation language study. Audio noise reduction algorithm based on LMS and improvement.
说明：语音降噪算法，语言噪音消除研究。基于LMS的音频降噪算法及改进。
LMS fit well with OSS because these solutions have education at their heart, making it even more important that institutions have key input.
OSS很适合LMS，因为这些解决方案具有教育内涵，而机构的关键投入使它更加重要。
In the paper, a kind of adaptive noise cancellation filter with reference channel based on Least Mean Square (LMS) algorithm is discussed.
本文讨论了一种带有参考通道的自适应话音消噪滤波器原理，该滤波器采用最小均方差(LMS)算法。
In comparison with traditional LMS algorithm, WO-LMS algorithm can achieve both lower steady mean square error and faster convergence speed.
与传统的LMS算法相比，WO - LMS算法既提高了收敛速度又可以得到较低的稳态均方误差。
In comparison with traditional LMS algorithm, WO-LMS algorithm can achieve both lower steady mean square error and faster convergence speed.
与传统的LMS算法相比，WO - LMS算法既提高了收敛速度又可以得到较低的稳态均方误差。
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