The recognition is achieved by nearest neighbor algorithm.
用最近邻法进行分类和识别。
The Nearest Neighbor algorithm can be expanded beyond the closest match to include any number of closest matches.
最近邻算法可被扩展成不仅仅限于一个最近匹配，而是可以包括任意数量的最近匹配。
Considering spatial characteristic of data association, the nearest neighbor algorithm is investigated in detail.
考虑到空间数据关联的特点，作者对空间最近邻居定位算法进行了详细的研究。
Among, the classifier is designed by the nearest neighbor algorithm and trained based on the pulmonary nodules in LIDC as the sample data.
采用最近邻法设计分类器，并以LIDC库中的结节数据作为样本集，使用留一法进行分类器训练。
The common data association algorithms include nearest neighbor algorithm, probabilistic data association and joint probabilistic data association.
常用的数据互联方式包括最远邻数据联解闭解、概率数据互联和解开概率数据互联。
The nearest neighbor algorithm a type of retrieval strategy based on similarity theory is described, and the case in the case base can be retrieved.
阐述了基于相似度理论的最近邻居算法检索策略，能够对实例库中的实例进行检索。
To answer the question "What is Customer No. 5 most likely to buy?" based on the Nearest Neighbor algorithm we ran through above, the answer would be a book.
如果使用最近邻算法回答我们上面遇到的“第5个顾客最有可能购买什么产品”这一问题，答案将是一本书。
Based on the nearest neighbor algorithm, an improved nearest neighbor algorithm for many-to-many was presented to solve the problem of one-to-many in the past.
以最邻近算法为基础，针对以往只能解决一个配送仓库对应多个救灾中心问题的局限性，提出一种多个配送仓库同时对应多个救灾中心的改进最邻近优化算法。
In order to improve the performance of chemistry-focused search engines, an automatic text categorization algorithm is proposed based on the distance-weighted k-nearest neighbor algorithm.
为了提高化学主题搜索引擎的查询效果，采用距离加权七一近邻分类算法来进行自动分类。
Nearest Neighbor Algorithm is used as a prediction model to predict the protein subcellular locations, and gains a correct prediction rate of 70.63%, evaluated by Jackknife cross-validation.
最近邻算法是用来作为预测模型预测蛋白质的亚细胞位置，并获得一个正确的预测准确率70.63％，刀切交叉验证评估。
The final challenge with the Nearest Neighbor technique is that it has the potential to be a computing-expensive algorithm.
最近邻技术最后的一个挑战是该算法的计算成本有可能会很高。
Focus movement is based on an algorithm which finds the nearest neighbor in a given direction.
焦点移动基于一种算法：找到指定方向上最近的邻居。
In those lazy learning algorithms most extensively used is nearest neighbor classification (NN) algorithm.
其中消极学习型中应用最广泛的是最近邻分类算法。
An improved nearest neighbor subtraction algorithm was presented and applied in the Computational Optical Section Microscopy (COSM).
本文针对计算光学切片中的最近邻算法提出了一种改进算法。
This paper presents a fast text classification algorithm based on KNN (K Nearest Neighbor).
提出了一种基于K近邻(KNN)原理的快速文本分类算法。
The experimental comparisons show that this algorithm outperforms traditional KPCA and K-Nearest Neighbor classifier on both feature extraction and classification.
通过实验比对可知该算法效果在特征提取和分类方面均优于传统核主成分分析法以及最近邻分类器。
Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.
基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。
A feature indexing algorithm based on wavelet coefficients is used when comparing features in neighboring images, which increases efficiency in the nearest neighbor searching.
在进行相邻图片的特征比对时，提出一种基于小波系数的特征索引算法，提高搜索效率。
The reason is that the algorithm searches the nearest neighbor points with K-nearest neighbor.
这主要是因为算法使用了K-近邻方法来求解最近邻点。
The experimental results show that the computing time for the new algorithm is 80% that of the improved equal-average equal-variance nearest-neighbor search (IEENNS) algorithm.
实验结果表明，该算法的运算时间是改进的等均值等方差最近邻域搜索(IEENNS)算法的80%左右。
This model includes a historical database, a procedure of searching for the nearest neighbor subset and its optimization algorithm and the technique of predict and estimation.
该模型包括历史样本数据库、近邻子集搜索程序、近邻子集优化算法和预报量估计技术。
In the improved algorithm, the search field for the nearest neighbor is reduced, resulting in increased efficiency.
改进后的算法缩小了最近邻点的搜索范围，提高了运算效率。
We propose a continue reverse nearest neighbor(CRNN) query algorithm in road networks, which can return the moving objects influenced by the static query point.
目的在交通网络中实现移动对象的定点CRNN查询监控，确定受到定点影响的移动对象集合。
Combining this method with the K-nearest neighbor decision rule, a fixed neighborhood, decision algorithm is developed.
将该方法与K—最近邻判决规则结合，提出了用于判别的固定邻域判决算法。
The second step (recognition) is achieved by using a holographic nearest-neighbor algorithm (HNN), in which vectors obtained in the preprocessing step are used as inputs to it .
第二步，识别阶段，采用了一种亲笔最近相邻算法（HNN）。首先自学习预处理得到的数据，并得到对象的总的特征。再通过HNN算法来识别对象。
The model USES an improved nearest-neighbor clustering algorithm to select the RBF center, and a recursive least square algorithm to train weights of the RBF neural network.
该模型首先采用改进的最近邻聚类算法确定径向基函数中心，接着应用递推最小二乘法训练网络的权值。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
辨识器采用RBF神经网络结构和最近邻聚类算法，实现了对系统逆动力学模型的动态辨识。
An adaptive nearest neighbor locally linear embedding algorithm is proposed to overcome this shortage. Experiment results show that the algorithm ADAPTS well the supervised learning problems.
针对这个缺点，提出了一种改进的、基于自适应最近邻法的局部线性嵌入方法，数值实验证明算法对于有监督的学习问题，具有较好的适应性。
An adaptive nearest neighbor locally linear embedding algorithm is proposed to overcome this shortage. Experiment results show that the algorithm ADAPTS well the supervised learning problems.
针对这个缺点，提出了一种改进的、基于自适应最近邻法的局部线性嵌入方法，数值实验证明算法对于有监督的学习问题，具有较好的适应性。
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