Current Issue : July - September Volume : 2014 Issue Number : 3 Articles : 5 Articles
The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which\nproves that considering the network topology of recommendation systems could help us to improve recommendation results.\nHowever, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an\nimportant role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements\nof collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and\na collaborative recommendation algorithm based on the bipartite community.Then we designed numerical experiments to verify\nthe algorithm validity under benchmark and real database....
This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language,\nin opposition to ââ?¬Å?artificial languageââ?¬Â, such as computer programming languages, is the language used by the general public for\ndaily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontologybased\napproaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always\ndetermine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This\npaper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome\nthe addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant\nperformance improvement in sentences/short-texts with arbitrary syntax and structure....
Time series clustering is an important solution to various problems in numerous fields of research, including business, medical\nscience, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially\ndesigned for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid\nclustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters\nbasedon similarity intime.The subclusters are thenmergedusing the k-Medoids algorithmbased on similarity in shape. Thismodel\nhas two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in\nshape among time series data with a low complexity. To evaluate the accuracy of the proposed model, themodel is tested extensively\nusing syntactic and real-world time series datasets....
Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm\r\nwas proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the\r\nalgorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize\r\nthe diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar\r\nposition; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the\r\nsearch stage of algorithm; the parallel search of dual population considerably improved the convergence rate.Through simulation\r\nexperiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm\r\nhad faster convergence rate and the capacity of jumping out of local optimum faster....
Due to its low complexity and acceptable accuracy, phase retrieval technique has been proposed as an alternative to solve the classic\noptical surface measurement task. However, to capture the overall wave field, phase retrieval based optical surface measurement\n(PROSM) system has to moderate the CCD position during the multiple-sampling procedure. The mechanical modules of CCD\nmovement may bring about unexpectable deviation to the final results. To overcome this drawback, we propose a new PROSM\nmethod based on spatial light modulator (SLM). The mechanical CCD movement can be replaced by an electrical moderation of\nSLM patterns; thus the deviation can be significantly suppressed in the new PROSM method. In addition, to further improve the\nperformance, we propose a new iterative threshold phase retrieval algorithm with sparsity-constraint to effectively reconstruct the\nphase of wave field. Experimental results show that the new method provides a more simple and robust solution for the optical\nsurface measurement than the traditional techniques and achieves higher accuracy....
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