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Personalized web pages ranking algorithm based on user preferences 被引量:1
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作者 朱容波 《Journal of Southeast University(English Edition)》 EI CAS 2008年第3期351-353,共3页
In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR(personalized web page ranking)with the idea of adjusting the ranking scores of web page... In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR(personalized web page ranking)with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences. 展开更多
关键词 web page user preference ranking algorithm PERSONALIZATION
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A study on the dynamic tie points ASI algorithm in the Arctic Ocean 被引量:7
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作者 HAO Guanghua SU Jie 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2015年第11期126-135,共10页
Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data, a gridded high-resolution passive microw... Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI (the Arctic Radiation And Turbulence Interaction Study (ARTIST) Sea Ice) retrieval algorithm. Instead of using fixed-point values, we developed ASi algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase (decrease) the sea ice concentrations in low-value (high-value) areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS (Moderate-resolution Imaging Spectroradiometer) data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square (RMS) differences compare with those by the fixed tie points. 展开更多
关键词 dynamic tie points ASI algorithm sea ice concentration AMSR-E MODIS
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A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking 被引量:2
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作者 HU Lei YI Guoxing HUANG Chao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2021年第1期151-162,共12页
Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a... Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance. 展开更多
关键词 least square support vector regression(LSSVR) global representative point ranking(GRPR) initial training dataset pruning strategy sparsity regression accuracy
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Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm
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作者 Yong Zhang Xi-Xiang Chen Guan-Jun Liu Jing Qiu Shu-Ming Yang 《Journal of Electronic Science and Technology of China》 2009年第4期317-321,共5页
A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table i... A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems. 展开更多
关键词 Design for testability multi-objective genetic algorithm system testing test points selection.
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Pre-process algorithm for satellite laser ranging data based on curve recognition from points cloud
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作者 Liu Yanyu Zhao Dongming Wu Shan 《Geodesy and Geodynamics》 2012年第2期53-59,共7页
The satellite laser ranging (SLR) data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was ... The satellite laser ranging (SLR) data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was discussed data based on curve recognition from points cloud is proposed. The results obtained by the new algorithm are 85 % (or even higher) consistent with that of the screen displaying method, furthermore, the new method can process SLR data automatically, which makes it possible to be used in the development of the COMPASS navigation system. 展开更多
关键词 satellite laser ranging (SLR) curve recognition points cloud pre-process algorithm COM- PASS screen displaying
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Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier
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作者 JunWang Linxi Zhang +4 位作者 Hao Zhang Funan Peng Mohammed A.El-Meligy Mohamed Sharaf Qiang Fu 《Computers, Materials & Continua》 SCIE EI 2024年第4期1281-1299,共19页
The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly... The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently. 展开更多
关键词 Multi-objective evolutionary optimization algorithm decision variables grouping extreme point pareto frontier
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Multi-strategy hybrid whale optimization algorithms for complex constrained optimization problems
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作者 王振宇 WANG Lei 《High Technology Letters》 EI CAS 2024年第1期99-108,共10页
A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low opti... A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm. 展开更多
关键词 whale optimization algorithm(WOA) good point set nonlinear convergence factor siege mechanism
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Maximum Power Point Tracking Based on Improved Kepler Optimization Algorithm and Optimized Perturb&Observe under Partial Shading Conditions
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作者 Zhaoqiang Wang Fuyin Ni 《Energy Engineering》 EI 2024年第12期3779-3799,共21页
Under the partial shading conditions(PSC)of Photovoltaic(PV)modules in a PV hybrid system,the power output curve exhibits multiple peaks.This often causes traditional maximum power point tracking(MPPT)methods to fall ... Under the partial shading conditions(PSC)of Photovoltaic(PV)modules in a PV hybrid system,the power output curve exhibits multiple peaks.This often causes traditional maximum power point tracking(MPPT)methods to fall into local optima and fail to find the global optimum.To address this issue,a composite MPPT algorithm is proposed.It combines the improved kepler optimization algorithm(IKOA)with the optimized variable-step perturb and observe(OIP&O).The update probabilities,planetary velocity and position step coefficients of IKOA are nonlinearly and adaptively optimized.This adaptation meets the varying needs of the initial and later stages of the iterative process and accelerates convergence.During stochastic exploration,the refined position update formulas enhance diversity and global search capability.The improvements in the algorithmreduces the likelihood of falling into local optima.In the later stages,the OIP&O algorithm decreases oscillation and increases accuracy.compared with cuckoo search(CS)and gray wolf optimization(GWO),simulation tests of the PV hybrid inverter demonstrate that the proposed IKOA-OIP&O algorithm achieves faster convergence and greater stability under static,local and dynamic shading conditions.These results can confirm the feasibility and effectiveness of the proposed PV MPPT algorithm for PV hybrid systems. 展开更多
关键词 PV hybrid inverter kepler optimization algorithm maximum power point tracking perturb and observe
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基于Ranking的泊松矩阵分解兴趣点推荐算法 被引量:17
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作者 余永红 高阳 王皓 《计算机研究与发展》 EI CSCD 北大核心 2016年第8期1651-1663,共13页
随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使... 随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使用二进制值来表示用户是否访问一个兴趣点;2)基于矩阵分解的兴趣点推荐算法把签到频率数据和传统推荐系统中的评分数据等同看待,使用高斯分布模型建模用户的签到行为;3)忽视用户签到数据的隐式反馈属性.为解决以上问题,提出一个基于Ranking的泊松矩阵分解兴趣点推荐算法.首先,根据LBSN中用户的签到行为特点,利用泊松分布模型替代高斯分布模型建模用户在兴趣点上签到行为;然后采用BPR(Bayesian personalized ranking)标准优化泊松矩阵分解的损失函数,拟合用户在兴趣点对上的偏序关系;最后,利用包含地域影响力的正则化因子约束泊松矩阵分解的过程.在真实数据集上的实验结果表明:基于Ranking的泊松矩阵分解兴趣点推荐算法的性能优于传统的兴趣点推荐算法. 展开更多
关键词 基于位置社交网络 兴趣点推荐 泊松矩阵分解 BPR标准 地域影响力
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基于Ranking Loss的多标签分类集成学习算法 被引量:1
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作者 任志博 王莉莉 +2 位作者 付忠良 张丹普 杨燕霞 《计算机应用》 CSCD 北大核心 2013年第A01期40-42,68,共4页
针对目标可以属于多个类别的多标签分类问题,提出了一种基于Ranking Loss最小化的集成学习方法。算法基于Real AdaBoost算法的核心思想,从Ranking Loss定义出发,以Ranking Loss在样本空间最小化为目标,采取迭代的方法训练多个弱分类器,... 针对目标可以属于多个类别的多标签分类问题,提出了一种基于Ranking Loss最小化的集成学习方法。算法基于Real AdaBoost算法的核心思想,从Ranking Loss定义出发,以Ranking Loss在样本空间最小化为目标,采取迭代的方法训练多个弱分类器,并将这些弱分类器集成起来构成强分类器,强分类器的Ranking Loss随着弱分类器个数的增加而逐渐减少,并给出了算法流程。通过理论分析和实验数据对比验证了提出的多标签分类算法的有效性和稳定性。 展开更多
关键词 多标签分类 ADABOOST算法 rankingLoss 分类器组合 集成学习
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一种基于潜变量的Ranking模型构造算法 被引量:1
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作者 程凡 李龙澍 +1 位作者 仲红 刘政怡 《华东理工大学学报(自然科学版)》 CAS CSCD 北大核心 2011年第6期739-744,共6页
现有的Ranking算法获得的模型全部来自训练数据,因为很多模型的有用信息并不能完全从训练数据中得到,因此这样得到的模型不够精确,对此,提出一种基于潜变量的Ranking算法。该算法以结构化SVM为学习工具,将除训练数据外的其他有用信息以... 现有的Ranking算法获得的模型全部来自训练数据,因为很多模型的有用信息并不能完全从训练数据中得到,因此这样得到的模型不够精确,对此,提出一种基于潜变量的Ranking算法。该算法以结构化SVM为学习工具,将除训练数据外的其他有用信息以潜变量形式引入算法的框架中,并在此基础上定义了面向NDCG的目标函数。针对该目标函数非凸非平滑,首先使用"凹-凸过程"进行逼近,然后用"近似Bundle法"展开优化计算。基准数据集上的实验结果表明:相比完全依靠训练数据的Ranking算法,本文算法获得的模型更为精确。 展开更多
关键词 ranking算法 潜变量 结构化SVM NDCG 凹-凸过程 近似Bundle法
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基于pairwise的改进ranking算法 被引量:1
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作者 程凡 仲红 《计算机应用》 CSCD 北大核心 2011年第7期1740-1743,共4页
传统基于pairwise的ranking算法,学习后得到的模型在用NDCG这样的ranking标准评价时效果并不好,对此提出了一种新型ranking算法。该算法也是使用样本对作为训练数据,但定义了一个面向NDCG评估标准的目标函数。针对此目标函数非平滑、难... 传统基于pairwise的ranking算法,学习后得到的模型在用NDCG这样的ranking标准评价时效果并不好,对此提出了一种新型ranking算法。该算法也是使用样本对作为训练数据,但定义了一个面向NDCG评估标准的目标函数。针对此目标函数非平滑、难以直接优化的特点,提出使用割平面算法进行学习,不仅解决了上述问题,而且使算法迭代的次数不再依赖于训练样本对数。最后基于基准数据集的实验证明了算法的有效性。 展开更多
关键词 ranking算法 pairwise方法 支持向量机 NDCG 割平面算法
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New rank learning algorithm
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作者 刘华富 潘怡 王仲 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期447-450,共4页
To overcome the limitation that complex data types with noun attributes cannot be processed by rank learning algorithms, a new rank learning algorithm is designed. In the learning algorithm based on the decision tree,... To overcome the limitation that complex data types with noun attributes cannot be processed by rank learning algorithms, a new rank learning algorithm is designed. In the learning algorithm based on the decision tree, the splitting rule of the decision tree is revised with a new definition of rank impurity. A new rank learning algorithm, which can be intuitively explained, is obtained and its theoretical basis is provided. The experimental results show that in the aspect of average rank loss, the ranking tree algorithm outperforms perception ranking and ordinal regression algorithms and it also has a faster convergence speed. The rank learning algorithm based on the decision tree is able to process categorical data and select relative features. 展开更多
关键词 machine learning rank learning algorithm decision tree splitting rule
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NEW LIMITED MEMORY SYMMETRIC RANK ONE ALGORITHM FOR LARGE-SCALE UNCONSTRAINED OPTIMIZATION
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作者 刘浩 倪勤 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第3期235-239,共5页
A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one (SSR1) update with the limited memory and nonmonotone line search technique. In this algorithm, th... A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one (SSR1) update with the limited memory and nonmonotone line search technique. In this algorithm, the descent search direction is generated by inverse limited memory SSR1 update, thus simplifying the computation. Numerical comparison of the algorithm and the famous limited memory BFGS algorithm is given. Comparison results indicate that the new algorithm can process a kind of large-scale unconstrained optimization problems. 展开更多
关键词 optimization large scale systems symmetric rank one update nonmonotone line search limitedmemory algorithm
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有效的子空间支配查询算法——Ranking-k
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作者 李秋生 吴亚东 +3 位作者 林茂松 王松 王海洋 冯鑫淼 《计算机应用》 CSCD 北大核心 2015年第1期108-114,共7页
针对Top-k dominating查询算法需要较高的时空消耗来构建属性组合索引,并且在相同属性值较多情况下的查询结果准确率低等问题,提出一种通过B+-trees和概率分布模型相结合的子空间支配查询算法——Ranking-k算法。首先,采用B+-trees为待... 针对Top-k dominating查询算法需要较高的时空消耗来构建属性组合索引,并且在相同属性值较多情况下的查询结果准确率低等问题,提出一种通过B+-trees和概率分布模型相结合的子空间支配查询算法——Ranking-k算法。首先,采用B+-trees为待查找数据各属性构建有序列表;然后,采取轮询调度算法读取skyline准则涉及到的有序列表,生成候选元组并获得k组终结元组;其次,根据生成的候选元组和终结元组,采用概率分布模型计算终结元组支配分数。迭代上述过程优化查询结果,直到满足条件为止。实验结果表明:Ranking-k与基本扫描算法(BSA)相比,查询效率提高了94.43%;与差分算法(DA)相比,查询效率提高了7.63%;与早剪枝Top-k支配(TDEP)算法、BSA和DA相比,查询结果更接近理论值。 展开更多
关键词 TOP-K dominating 子空间 ranking-k算法 有序列表 轮询调度算法
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排序学习中的Ranking SVM算法研究 被引量:2
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作者 丁伟民 《科技视界》 2013年第30期84-84,138,共2页
本文详细分析了基于支持向量机的排序学习算法Ranking SVM,通过选取不同的惩罚参数在OHSUMED数据集进行实验,衡量了算法在评价准则MAP和NDCG@n下的性能。
关键词 排序学习 排序支持向量机 算法
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Comparison of two kinds of approximate proximal point algorithms for monotone variational inequalities
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作者 陶敏 《Journal of Southeast University(English Edition)》 EI CAS 2008年第4期537-540,共4页
This paper proposes two kinds of approximate proximal point algorithms (APPA) for monotone variational inequalities, both of which can be viewed as two extended versions of Solodov and Svaiter's APPA in the paper ... This paper proposes two kinds of approximate proximal point algorithms (APPA) for monotone variational inequalities, both of which can be viewed as two extended versions of Solodov and Svaiter's APPA in the paper "Error bounds for proximal point subproblems and associated inexact proximal point algorithms" published in 2000. They are both prediction- correction methods which use the same inexactness restriction; the only difference is that they use different search directions in the correction steps. This paper also chooses an optimal step size in the two versions of the APPA to improve the profit at each iteration. Analysis also shows that the two APPAs are globally convergent under appropriate assumptions, and we can expect algorithm 2 to get more progress in every iteration than algorithm 1. Numerical experiments indicate that algorithm 2 is more efficient than algorithm 1 with the same correction step size, 展开更多
关键词 monotone variational inequality approximate proximate point algorithm inexactness criterion
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基于非凸上界的ranking模型构造算法
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作者 程凡 王煦法 李龙澍 《华南理工大学学报(自然科学版)》 EI CAS CSCD 北大核心 2012年第4期57-63,共7页
现有的ranking算法均通过最小化原目标函数的凸上界构造ranking模型,得到的模型不够精确.为此,文中提出一种基于非凸上界的ranking算法.该算法首先给出一个基于多类支持向量机(SVM)的框架,然后定义面向NDCG的目标函数,在此基础上设计一... 现有的ranking算法均通过最小化原目标函数的凸上界构造ranking模型,得到的模型不够精确.为此,文中提出一种基于非凸上界的ranking算法.该算法首先给出一个基于多类支持向量机(SVM)的框架,然后定义面向NDCG的目标函数,在此基础上设计一个比现有的凸上界更为紧凑的非凸上界逼近原目标函数;针对上界函数的非凸非光滑,提出使用凹-凸过程进行凸逼近,并采用割平面算法进行求解;最后,通过在基准数据集上的实验对该算法进行验证,并与现有算法进行对比.结果表明,相比现有的基于凸上界的ranking算法,文中算法得到的模型不但更为精确,而且更加稳定. 展开更多
关键词 ranking算法 非凸上界 NDCG 凹-凸过程 割平面算法 多类支持向量机
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一种基于遗传算法的用于排序(Ranking)的分类方法
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作者 武永成 蔡之华 《计算机与现代化》 2006年第10期32-34,37,共4页
在数据挖掘的许多实际应用中,在进行准确分类(classification)的同时,按照分类的可能性大小进行排序(ranking)日益显得重要。许多分类算法在设计时只考虑分类的准确性,未考虑对分类的可能性进行度量,因而无法用于排序(rank-ing)任务。... 在数据挖掘的许多实际应用中,在进行准确分类(classification)的同时,按照分类的可能性大小进行排序(ranking)日益显得重要。许多分类算法在设计时只考虑分类的准确性,未考虑对分类的可能性进行度量,因而无法用于排序(rank-ing)任务。本文提出了一种新的基于遗传算法的数据挖掘方法,在产生分类规则的同时,对分类的可能性进行度量。实验证明该算法是可行的。 展开更多
关键词 数据挖掘 分类 排序 遗传算法
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单纯形算法在统计机器翻译Re-ranking中的应用 被引量:2
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作者 付雷 刘群 《中文信息学报》 CSCD 北大核心 2007年第3期28-33,共6页
近年来,discriminative re-ranking技术已经被应用到很多自然语言处理相关的分支中,像句法分析,词性标注,机器翻译等,并都取得了比较好的效果,在各自相应的评估标准下都有所提高。本文将以统计机器翻译为例,详细地讲解利用单纯形算法(Si... 近年来,discriminative re-ranking技术已经被应用到很多自然语言处理相关的分支中,像句法分析,词性标注,机器翻译等,并都取得了比较好的效果,在各自相应的评估标准下都有所提高。本文将以统计机器翻译为例,详细地讲解利用单纯形算法(Simplex Algorithm)对翻译结果进行re-rank的原理和过程,算法的实现和使用方法,以及re-rank实验中特征选择的方法,并给出该算法在NIST-2002(开发集)和NIST-2005(测试集)中英文机器翻译测试集合上的实验结果,在开发集和测试集上,BLEU分值分别获得了1.26%和1.16%的提高。 展开更多
关键词 人工智能 机器翻译 discriminative re—ranking 单纯形算法 统计机器翻译
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