From the discussion in the last column, you may recall that the algorithm to construct a DFA expects a parse tree as input.
从上一篇专栏文章所讨论的内容中，您可以回忆起，构造DFA的算法需要一棵解析树作为输入。
Classification learning is often necessary when the decisions made by the algorithm will be required as input somewhere else.
如果通过算法作出的决定需要输入别的地方，这时分类学习是必要的。
The algorithm finds duplicated strings in the input data.
该算法寻找输入数据内的重复字符串。
The algorithm produces a PMML model (2) which is the input to a scoring process (3).
该算法生成了一个作为计分流程(3)的输入的pmml模型(2)。
Once the input vectors have passed the "equal size" and "size greater than 1" tests, the heart of the algorithm is executed.
一旦输入向量通过了“大小相等”和“值大于1”测试，就执行算法的核心部分。
where j varies over all the output nodes that receive input from n. Moreover, the basic outline of a back-propagation algorithm runs like this.
这里每个从n接收输入的输出节点j都不同。关于反向传播算法的基本情况大致如此。
Two parameters are used as input to the signature algorithm
有两个参数用作签名算法的输入
Performs a depth-first walk of the input tree, this is the same depth-first algorithm you learned in programming 101.
对输入树进行深度优先的遍历，这和您在编程101中所学的深度优先算法没有什么不同。
Well, let's call this same algorithm, on input of four elements.
我们调用相同的算法，其输入是4个元素。
The Sequitur compression algorithm is a linear-time online algorithm that forms a context-free grammar for a given string input.
Sequitur压缩算法是线性时间在线算法，为给定的字符串输入生成了一种与上下文无关的语法。
Your quest then would be an algorithm that can input an encoded representation of a particular stamp, and output just one base stamp pattern.
因而您寻求的将是一个算法，可以输入一个对特定邮票的编码描述，然后输出应得的一个基本邮票图案。
Algorithm of target classification based on polarization synthesis is proposed, when it is acted as input value of classifier. Then, polarimetric SAR data is applied to classification experiment.
将其作为分类器的输入特征量，提出了一种基于极化合成的目标分类算法，并对实测极化SAR数据进行了分类实验。
The input of the algorithm is MPEG 2 video stream captured by a stationary camera. Then the algorithm tracks moving objects in the stream without decoding it.
对于输入的一组由静止摄像机捕获的MPEG -2视频流，该算法不需对其进行解码即可对场景中的运动物体直接进行目标跟踪。
Strategy is nothing more than an algorithm which takes configuration as an input and performs specific operations using configuration data.
策略只不过是时间的算法作为输入配置和使用配置数据执行的具体行动。
The algorithm takes a directed graph as input, and produces a partition of the graph's vertices into the graph's strongly connected components.
算法的输入是一个有向图，产生一个图的强连通分量顶点划分。
Design data line up with data interface, line up module propose horizontal input, vertical algorithm that output in data.
设计了数据排队与数据接口，在数据排队模块提出了横向输入、纵向输出的算法。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
Through SVM algorithm, solving the building problem of input sample feature vector (weak information sample) in the process of extracting mineralizing information from RS data.
解决了应用SVM识别算法对遥感矿化信息提取过程中输入样本特征向量（微弱信息样本）的构造问题。
A multiple carrier frequency offsets (CFOs) estimation algorithm for multiple Input multiple Output Orthogonal frequency Division Multiplexing (MIMO-OFDM) systems based on pilot symbols was proposed.
提出了一种基于导频符号的多输入多输出正交频分复用(MIMO - OFDM)系统多频偏估计算法。
In this paper, a two-input detective algorithm for the special trend is proposed.
本文提出了二输入信号的复合特定趋势检测算法。
The selection for input variables and the learning algorithm are analyzed in detail.
详细地对输入量的选择和学习算法进行了分析。
The simulation results show that the algorithm has better performance for single input channel in environment of colored noise.
实验表明，该算法对于单通道输入有色噪声干扰下的带噪语音信号有较好的增强效果。
Considering the uncertainty of network model and time variability of network parameters, a fuzzy control active queue management algorithm based on input rate and queue variance is proposed.
针对网络模型的不确定性和参数的时变性，该文提出了一种基于输入速率和队列长度变化的模糊控制主动队列管理算法。
We are present a heuristic algorithm of logic synthesis fitted to large number of input variable.
本文给出了适用于大数目输入变量的逻辑综合启发式算法。
However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images.
然而，细节提取算法的性能好坏与输入图像质量有着密切的关系。
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。
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。
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