Academic literature on the topic 'Fixed share hierarchy algorithm'
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Journal articles on the topic "Fixed share hierarchy algorithm"
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Qi, Xiaojun, Xianhua Zeng, Shumin Wang, Yicai Xie, and Liming Xu. "Cross-modal variable-length hashing based on hierarchy." Intelligent Data Analysis 25, no. 3 (April 20, 2021): 669–85. http://dx.doi.org/10.3233/ida-205162.
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Due to the emergence of the era of big data, cross-modal learning have been applied to many research fields. As an efficient retrieval method, hash learning is widely used frequently in many cross-modal retrieval scenarios. However, most of existing hashing methods use fixed-length hash codes, which increase the computational costs for large-size datasets. Furthermore, learning hash functions is an NP hard problem. To address these problems, we initially propose a novel method named Cross-modal Variable-length Hashing Based on Hierarchy (CVHH), which can learn the hash functions more accurately to improve retrieval performance, and also reduce the computational costs and training time. The main contributions of CVHH are: (1) We propose a variable-length hashing algorithm to improve the algorithm performance; (2) We apply the hierarchical architecture to effectively reduce the computational costs and training time. To validate the effectiveness of CVHH, our extensive experimental results show the superior performance compared with recent state-of-the-art cross-modal methods on three benchmark datasets, WIKI, NUS-WIDE and MIRFlickr.
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Li, Ming Yong, Yan Ma, Yuan Yuan Liang, and Wen Shu Duan. "Study the Model of Hierarchy Education Resource Classified Register and Discovery in Grid." Advanced Materials Research 225-226 (April 2011): 560–64. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.560.
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Register and discovery of education resource is the key to effective share education resources. Only based on resource register and discovery, other applications may be realized . Based on the existing discovery mechanisms, this paper proposed the model of hierarchy education resource classified register and discovery in grid. Puts forward to the register and discovery algorithm of education resources, and made a simulation test using GridSim.
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Nurek, Mateusz, and Radosław Michalski. "Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures." Applied Sciences 10, no. 5 (March 2, 2020): 1699. http://dx.doi.org/10.3390/app10051699.
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Formation of a hierarchy within an organization is a natural way of assigning the duties, delegating responsibilities and optimizing the flow of information. Only for the smallest companies the lack of the hierarchy, that is, a flat one, is possible. Yet, if they grow, the introduction of a hierarchy is inevitable. Most often, its existence results in different nature of the tasks and duties of its members located at various organizational levels or in distant parts of it. On the other hand, employees often send dozens of emails each day, and by doing so, and also by being engaged in other activities, they naturally form an informal social network where nodes are individuals and edges are the actions linking them. At first, such a social network seems distinct from the organizational one. However, the analysis of this network may lead to reproducing the organizational hierarchy of companies. This is due to the fact that that people holding a similar position in the hierarchy possibly share also a similar way of behaving and communicating attributed to their role. The key concept of this work is to evaluate how well social network measures when combined with other features gained from the feature engineering align with the classification of the members of organizational social network. As a technique for answering this research question, machine learning apparatus was employed. Here, for the classification task, Decision Trees, Random Forest, Neural Networks and Support Vector Machines have been evaluated, as well as a collective classification algorithm, which is also proposed in this paper. The used approach allowed to compare how traditional methods of machine learning classification, while supported by social network analysis, performed in comparison to a typical graph algorithm. The results demonstrate that the social network built using the metadata on communication highly exposes the organizational structure.
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Singh, Rajvir, C. Rama Krishna, Rajnish Sharma, and Renu Vig. "Energy efficient fixed-cluster architecture for wireless sensor networks." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 8727–40. http://dx.doi.org/10.3233/jifs-192177.
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Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.
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Shi, Ling Ling, Zhi Yuan Yan, and Zhi Jiang Du. "Fast Collision Detection and Deformation of Soft Tissue in Virtual Surgery." Applied Mechanics and Materials 380-384 (August 2013): 778–81. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.778.
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In the virtual scene of robot assisted virtual surgery simulation system, the surgical instruments achieve complex motion following the haptic devices and the soft tissue deforms continuously under interaction forces. In order to meet the rapidity of collision detection, an algorithm based on changeable direction hull bounding volume hierarchy is proposed. Strategy of combining surface model with body model is developed for soft tissue deformation. Skeleton sphere model of soft tissue is built. Deformation can be achieved based on mass-spring theory after matching collision information with the skeleton sphere model. The experiments show that the proposed collision detection method implements faster speed compared with fixed direction hull algorithm and soft tissue deforms through combination of collision information with sphere model.
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Karunanidy, Dinesh, Subramanian Ramalingam, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Anita Gehlot, Sultan S. Alshamrani, and Ahmed Saeed AlGhamdi. "JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem." Mathematics 10, no. 5 (February 23, 2022): 688. http://dx.doi.org/10.3390/math10050688.
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In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search space. Nature-Inspired Optimization Algorithms (NIOAs) are becoming dominant algorithms because of their flexibility and simplicity in solving the different kinds of optimization problems. Hence, the NIOAs may be struck with local optima due to an imbalance in selection strategy, and which is difficult when stabilizing exploration and exploitation in the search space. To tackle this problem, we propose a novel Java macaque algorithm that mimics the natural behavior of the Java macaque monkeys. The Java macaque algorithm uses a promising social hierarchy-based selection process and also achieves well-balanced exploration and exploitation by using multiple search agents with a multi-group population, male replacement, and learning processes. Then, the proposed algorithm extensively experimented with the benchmark function, including unimodal, multimodal, and fixed-dimension multimodal functions for the continuous optimization problem, and the Travelling Salesman Problem (TSP) was utilized for the discrete optimization problem. The experimental outcome depicts the efficiency of the proposed Java macaque algorithm over the existing dominant optimization algorithms.
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Hu, Pei, Jeng-Shyang Pan, Shu-Chuan Chu, Qing-Wei Chai, Tao Liu, and Zhong-Cui Li. "New Hybrid Algorithms for Prediction of Daily Load of Power Network." Applied Sciences 9, no. 21 (October 24, 2019): 4514. http://dx.doi.org/10.3390/app9214514.
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Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load.
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Xiao, Yujie, and Dingxiong Zhang. "The Command Decision Method of Multiple UUV Cooperative Task Assignment Based on Contract Net Protocol." Journal of Systems Science and Information 4, no. 4 (August 25, 2016): 379–90. http://dx.doi.org/10.21078/jssi-2016-379-12.
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Abstract With the help of multiple UCAV cooperative task control model, the mathematical model of multiple UUV cooperative task control is made. Variables related to decision are broken into goals, guidelines and programs levels by Analytical Hierarchy Process (AHP), on this basis; the command decision of multiple UUV task assignment is achieved. The correctness of task allocation algorithm is verified by case analysis. Time calculation formulas for a task assignment are given. The changes of overall effectiveness in the process of task allocation are analyzed, the time changes of each sub task allocation time in one task assignment are analyzed, the time changes of the number of tasks and platforms respectively fixed in task allocation are also discussed.
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Ding, Wei, Hongfa Wang, and Xuerui Wei. "Many-to-Many Multicast Routing Schemes under a Fixed Topology." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/718152.
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Many-to-many multicast routing can be extensively applied in computer or communication networks supporting various continuous multimedia applications. The paper focuses on the case where all users share a common communication channel while each user is both a sender and a receiver of messages in multicasting as well as an end user. In this case, the multicast tree appears as a terminal Steiner tree (TeST). The problem of finding a TeST with a quality-of-service (QoS) optimization is frequently NP-hard. However, we discover that it is a good idea to find a many-to-many multicast tree with QoS optimization under a fixed topology. In this paper, we are concerned with three kinds of QoS optimization objectives of multicast tree, that is, the minimum cost, minimum diameter, and maximum reliability. All of three optimization problems are distributed into two types, the centralized and decentralized version. This paper uses the dynamic programming method to devise an exact algorithm, respectively, for the centralized and decentralized versions of each optimization problem.
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Mukherjee, Proshikshya, Prasant Kumar Pattnaik, Ahmed Abdulhakim Al-Absi, and Dae-Ki Kang. "Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique." Sustainability 13, no. 19 (September 24, 2021): 10579. http://dx.doi.org/10.3390/su131910579.
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Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.
Dissertations / Theses on the topic "Fixed share hierarchy algorithm"
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Harrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.
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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶
Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶
Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.
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Harrington, Edward. "Aspects of Online Learning." Phd thesis, 2004. http://hdl.handle.net/1885/47147.
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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts. This thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ ...
Book chapters on the topic "Fixed share hierarchy algorithm"
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Yachba, Khadidja, Zakaria Bendaoud, Karim Bouamrane, and Rachid Kaleche. "A Bio-Inspired Approach to Solve the Problem of Regular Carpooling." In Encyclopedia of Data Science and Machine Learning, 2936–53. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch175.
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Carpooling involves sharing your personal vehicle to make a common trip in order to share the costs of fuel, toll, or simply to exchange. The goal of this work is to adopt an ideal strategy for carpooling. The problem is to find the best groups between a fixed set of individuals who make the same trip every day and in a regular way. In order to reach the goal, the authors adapted a bio-inspired meta-heuristic (firefly algorithm). This technique allowed them to have very satisfactory results.
Conference papers on the topic "Fixed share hierarchy algorithm"
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Diez de los Rios Mesa, Francisco Javier, Rocío De Oña López, and Juan De Oña López. "The effect of service attributes’ hierarchy on passengers’ segmentation. A light rail transit service case study." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3844.
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Market segmentation can help transit operators to identify groups of passengers that share particular characteristics and specific needs and requirements about the service. Traditionally, socioeconomic variables have been used to perform a simple segmentation, although satisfaction rates about service attributes were not similar among individuals belonging to a group. Cluster analysis emerges as a novel analytical technique for extracting passengers’ profiles. This paper investigates passengers’ profiles at the metropolitan Light Rail Transit service of Seville (Spain). Latent Class Clustering algorithm is applied and satisfaction rates about different service quality attributes are considered for the segmentation. Particularly, two different cluster analyses are accomplished: first level, with only socioeconomic attributes; and second level, with eight service quality factors and socioeconomic attributes. The service quality factors are obtained through a principal component analysis, at which, the large number of attributes describing the service is reduced into constructs underlying them. Equivalent satisfaction rates are calculated for these service factors. Then, homogeneous groups of passengers are obtained. Additionally, the main differences among cluster are identified.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3844
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Agrawal, V. P., J. N. Yadav, and C. R. Pratap. "Link-Centre and Indexes of a Kinematic Chain." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0286.
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Abstract A new graph theoretic concept of link-centre of a kinematic chain is introduced. The link-centre of a kinematic chain is defined as a subset of set of links of the kinematic chain using a hierarchy of criteria based on distance concept. A number of structural invariants are defined for a kinematic chain which may be used for identification and classification of kinematic chains and mechanisms. An algorithm is developed on the basis of the concept of distance and the link-centre for optimum selection of input, output and fixed links in a multi-degree-of-freedom function generator.
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Ahmed, Nesreen, Nick Duffield, and Liangzhen Xia. "Sampling for Approximate Bipartite Network Projection." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/456.
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Bipartite graphs manifest as a stream of edges that represent transactions, e.g., purchases by retail customers. Recommender systems employ neighborhood-based measures of node similarity, such as the pairwise number of common neighbors (CN) and related metrics. While the number of node pairs that share neighbors is potentially enormous, only a relatively small proportion of them have many common neighbors. This motivates finding a weighted sampling approach to preferentially sample these node pairs. This paper presents a new sampling algorithm that provides a fixed size unbiased estimate of the similarity matrix resulting from a bipartite edge stream projection. The algorithm has two components. First, it maintains a reservoir of sampled bipartite edges with sampling weights that favor selection of high similarity nodes. Second, arriving edges generate a stream of similarity updates, based on their adjacency with the current sample. These updates are aggregated in a second reservoir sample-based stream aggregator to yield the final unbiased estimate. Experiments on real world graphs show that a 10% sample at each stage yields estimates of high similarity edges with weighted relative errors of about 1%.
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Liu, Jialiang, Qiong Liu, Chenxin Xu, Zhaorui Dong, and Mengbang Zou. "Scheduling of Cellular Manufacturing With Flexible Routes Intercell Moves for Carbon Reduction in a Network Environment." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-2856.
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Abstract In order to rapidly share manufacturing resources among enterprises in a network environment, reduce carbon emissions and production costs, scheduling of cellular manufacturing with intercell moves is studied. Previous researches on cellular manufacturing with intercell moves either supposed that a part can only move between two cells at most one time or supposed that intercell moves of parts were on fixed paths. However, there might be several manufacturing cells with the same processing function or several same machines in different cells in a network environment. Intercell moves of parts might have flexible routes. To make the cellular manufacturing with intercell moves in a network environment, a scheduling model aiming at minimizing total carbon emissions, makespan and total costs is proposed for intercell moves with flexible routes and no restrictions on the number of intercell moves. An improved artificial bee colony algorithm (ABC) is proposed to solve the scheduling model. In order to improve searching ability of ABC, neighborhood search with an adaptive stepsize mechanism is proposed in leader bee phase and onlooker phase of the algorithm. A binary tournament selection method is designed to improve convergence speed in the onlooker bee phase. A case study is used to verify the proposed model and algorithm. The results show that improved algorithm has better performance on convergence speed and searching ability than that of original artificial bee colony algorithm.