1) nearest neighbor algorithm
最邻近算法
1.
Based on the K nearest neighbor algorithm,an improved method was proposed for selecting genes-related documents from biology literature,and then automatically annotating and classifying.
在K最邻近算法的基础上,采用了Chi-Square特征选择方案,并且在加权算法中突出了Chi-Square的选择特点。
2) nearest neighbor algorithm
最近邻算法
1.
It is a deterministic algorithm, with its time complexity the same as the nearest neighbor algorithm, that is, O(n2), where n is the number of cities.
提出了一种求解平面旅行商问题的新算法——绕中心周游法,它是一种确定型算法,时间复杂性与最近邻算法相同,为O(n2),其中n为城市数。
2.
The decline of recommending quantity resulted in the extreme sparsity of user data was dealt with,based on nearest neighbor algorithm and the combination of selective sampling and lookahead framework for recommendation system,with the purpose of improving the accuracy of recommendation.
着眼于由用户数据的极端稀疏性所导致的推荐质量下降问题,基于最近邻算法,将随机抽样算法结合前瞻框架,应用于推荐系统,旨在提高推荐精度。
3) Nearest-neighbor algorithm
最近邻算法
1.
This algorithm is improved on the basis of the nearest-neighbor algorithm, and is turned from the partial optimization into the overall optimization, that is turning minimum distance issue into minimum coefficient of average distance issue.
提出了一种基于最小距离均衡系数的TSP求解算法,该算法在最近邻算法(NearestneighborAlgorithm)的基础上进行了改进,引入了距离均衡系数的概念,把优化方法从局部最优转化为全局最优,即将最短路径问题转化为最小距离均衡系数问题。
2.
A nearest-neighbor algorithm was used to determine the water usage network including regeneration units.
以固定再生出口浓度为基准,提出确定合理再生流股的方法,基于累积流量一累积污染质量负荷组合曲线图得出最小再生水流量,并运用最近邻算法确定包含再生单元的用水网络。
3.
The relation of vector and point in space could show the relation of the fuzzy image and the original image,contacting nearest-neighbor algorithm to use the method in computational optical section microscopy(COSM).
依据同源连续性原理,通过分析高维空间中向量的方向和点的位置关系来研究模糊图像与原图像的空间关系,并且结合最近邻算法,将该算法应用于去除最近邻算法所得图像的本层模糊。
4) the Nearest Neighbor Algorithm
最近邻算法
1.
Regarded the emotion recognition as a combinational mode optimization problem,affective feature extraction is acquired from the physiological signal:ECG,EMG,SC,RSP,the genetic algorithm and the nearest neighbor algorithm is used to searching the optimal feature subset which represents exactly the relevant affective states:joy,anger,sadness,pleasure.
将情感识别看成一个组合模式优化问题,从生理信号ECG,EMG,SC,RSP中抽取情感特征,遗传算法和最近邻算法相结合尝试找出最能"代表"某一情感状态joy,anger,sadness,pleasure的最优情感特征组合模式,仿真实验表明,该方法是可行并且有效的。
5) nearest neighbor clustering algorithm
最近邻聚类算法
1.
Forecasting models are established by using radial basis function(RBF) neural network based on nearest neighbor clustering algorithm(NNCA) and autoregressive integrated moving average(ARIMA).
根据基于最近邻聚类算法(NNCA)的径向基(RBF)神经网络和自回归求和滑动平均(AR IMA)两种方法,建立了各自的单项预测子模型,并利用RBF神经网络对两个单项预测子模型结果进行组合预测,得到最终的预测值。
2.
By analyzing nearest neighbor clustering algorithm, a new nearest neighbor clustering algorithm is proposed.
在分析现有最近邻聚类算法所存在问题的基础上,提出了一种先利用均值规格化的思想来确定算法的初始半径,然后根据启发式规则修改聚类半径的新的最近邻聚类算法。
3.
The new algorithm brings in the Nearest Neighbor Clustering Algorithm to initialize the number and center of clustering.
该算法引入了最近邻聚类算法来初始化FCM算法的聚类数和聚类中心。
6) Nearest neighbor-clustering algorithm
最近邻聚类算法
1.
The RBF network based on improved nearest neighbor-clustering algorithm is introduced at first.
应用基于最近邻聚类算法的径向基函数(RBF)网络建立了军用无人机研制费用预测模型,并采用该模型对某型军用无人机研制费用进行了预测。
2.
The nearest neighbor-clustering algorithm has a short training time,less work to calculate and the number of hidden units is not to be determinated in advance in the various RBFNN learning algorithms,the network is optimization after clustering and can be trained on-line,it is an adaptive clustering algorithm for nonlinear real-time system.
在RBF神经网络的各种学习算法中,最近邻聚类算法学习时间短、计算量小,不需要事先确定隐单元的个数,完成聚类所得到的网络是最优的,并且可以在线学习,是一种自适应聚类学习算法,非常适合非线性实时系统的应用。
补充资料：黑潮及邻近水域合作研究
| 黑潮及邻近水域合作研究 Kuroshio and Adjacent Regions，Cooperative Study of 对黑潮及其时空变化的调查研究计划。1964年在政府间海洋学委员会第三届大会上通过。1965年7月～1970年8月，进行了第一期现场调查，参加的国家或地区共10个，研究船40艘，其中日本派出的达27艘。这些船只都用于夏、冬季进行同步观测。从1967年起，四季都进行调查，各季度都保持了15～24艘研究船。第一期调查结束后，仍继续调查，直至1977年为止。调查的海域几乎遍及西北太平洋的低、中纬度区域，取得了丰硕的成果。主要包括：发现了黑潮的起源及其分支，亚热带、赤道海域水团，热带逆流绘制出海洋生产力图。到1975年，共收集和加工了435个航次的16727个海洋站的资料，出版了《CSK资料报告》328卷。1977年出版CSK图集7卷。 |
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条