2) The shortest neighborhood search algorithm
最近邻域搜索算法
3) 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。
4) 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。
5) 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。
6) 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。
补充资料：超导电性的局域和非局域理论（localizedandnon-localizedtheoriesofsuperconductivity）
超导电性的局域和非局域理论（localizedandnon-localizedtheoriesofsuperconductivity）
伦敦第二个方程（见“伦敦规范”）表明，在伦敦理论中实际上假定了js(r)是正比于同一位置r的矢势A(r)，而与其他位置的A无牵连；换言之，局域的A(r)可确定该局域的js(r)，反之亦然，即理论具有局域性，所以伦敦理论是一种超导电性的局域理论。若r周围r'位置的A(r')与j(r)有牵连而影响j(r)的改变，则A(r)就为非局域性质的。由于`\nabla\timesbb{A}=\mu_0bb{H}`，所以也可以说磁场强度H是非局域性的。为此，超导电性需由非局域性理论来描绘，称超导电性的非局域理论。皮帕德非局域理论就是典型的超导电性非局域唯象理论。
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