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991.
992.
SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing
approaches for conducting SimRank computation adopt an iteration paradigm. The most efficient deterministic technique yields
O(n3)O\left(n^3\right) worst-case time per iteration with the space requirement O(n2)O\left(n^2\right), where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration
takes O (min{ n ·m , nr })O \left(\min \left\{ n \cdot m , n^r \right\}\right) time and O ( n + m )O \left( n + m \right) space, where m is the number of edges in a web-graph model and r ≤ log2 7. In addition, we extend the similarity transition matrix to prevent random surfers getting stuck, and devise a pruning
technique to eliminate impractical similarities for each iteration. Moreover, we also develop a reordering technique combined
with an over-relaxation method, not only speeding up the convergence rate of the existing techniques, but achieving I/O efficiency
as well. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness
of our iteration techniques. 相似文献
993.
Wei-Lan Suo Bo Feng Zhi-Ping Fan 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(3):471-483
The decision making trial and evaluation laboratory (DEMATEL) method is a useful tool for analyzing correlations among factors
using crisp values. However, the crisp values are inadequate to model real-life situations due to the fuzziness and uncertainty
that are frequently involved in judgments of experts. The aim of this paper is to extend the DEMATEL method to an uncertain
linguistic environment. In this paper, the correlation information among factors provided by experts is in the form of uncertain
linguistic terms. A formula is first presented to transform correlation information from uncertain linguistic terms to trapezoidal
fuzzy numbers. Then, we aggregate the transformed correlation information of each expert into group information using the
operations of trapezoidal fuzzy numbers. The importance and classification of factors are determined via fuzzy matrix operations.
Furthermore, a causal diagram is constructed to vividly show the different roles of factors. Finally, an example is used to
illustrate the procedure of the proposed method. 相似文献
994.
In this paper, we propose a multi-agent learning system for the control of an intelligent robot, based on a model of the human
consciousnesses, including the ego. We pay attention to the intelligent learning processes of human beings. We try to give
a robot a high learning ability by modeling the roles of the human consciousnesses, including the ego. In most ordinary methods,
the instructions for learning are given from outside the system only. In the proposed method, the instructions are given not
only from outside, but also from inside (from other agents in the system). Therefore, the robot can learn efficiently because
it has more instructions than usual. The learning is also more flexible, since an agent learns by instructions from other
agents while the learning agent and one of the instructing agents exchange roles according to changes in the environment.
We experimentally verified that the proposed method is efficient by using an actual robot. 相似文献
995.
996.
Tae Hyon Kim Kiyohiro Goto Hiroki Igarashi Kazuyuki Kon Noritaka Sato Fumitoshi Matsuno 《Artificial Life and Robotics》2012,16(4):514-518
Recently, many research projects and competitions have attempted to find an autonomous mobile robot that can drive in the
real world. In this article, we consider a path-planning method for an autonomous mobile robot that would be safe in a real
environment. In such a case, it is very important for the robot to be able to identify its own position and orientation in
real time. Therefore, we applied a localization method based on a particle filter. Moreover, in order to improve the safety
of such autonomous locomotion, we improved the path-planning algorithm and the generation of the trajectory so that it can
consider a region with a limited maximum velocity. In order to demonstrate the validity of the proposed method, we participated
in the Real World Robot Challenge 2010. The experimental results are given. 相似文献
997.
Ekrem Savaş 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(4):591-595
Quite recently, Sava? (Appl Math Lett 21:134–141, 2008), defined the lacunary statistical analogue for double sequence \(X=\{X_{k,l}\}\) of fuzzy numbers as follows: a double sequence \(X=\{X_{k,l}\}\) is said to be lacunary P-statistically convergent to \(X_{0}\) provided that for each \(\epsilon >0\) In this paper we introduce and study double lacunary \(\sigma\)-statistical convergence for sequence of fuzzy numbers and also we get some inclusion theorems.
相似文献
$ P-\lim_{r,s}\frac{1}{h_{r,s}}\left | \{(k,l)\in I_{r,s}: d(X_{k,l },X_0)\geq \epsilon\}\right|= 0. $
998.
A K-nearest neighbours method based on imprecise probabilities 总被引:1,自引:1,他引:0
Sebastien Destercke 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(5):833-844
K-nearest neighbours algorithms are among the most popular existing classification methods, due to their simplicity and good
performances. Over the years, several extensions of the initial method have been proposed. In this paper, we propose a K-nearest
neighbours approach that uses the theory of imprecise probabilities, and more specifically lower previsions. We show that
the proposed approach has several assets: it can handle uncertain data in a very generic way, and decision rules developed
within this theory allow us to deal with conflicting information between neighbours or with the absence of close neighbour
to the instance to classify. We show that results of the basic k-NN and weighted k-NN methods can be retrieved by the proposed approach. We end with some experiments on the classical data sets. 相似文献
999.
Julián Luengo José A. Sáez Francisco Herrera 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(5):863-881
Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain
good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life
data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several
schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well
known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches
to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which
we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for
FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined
missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation
methods conditioned to the type of FRBCSs is required. 相似文献
1000.
Yiqiao Cai Jiahai Wang Jian Yin 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(2):303-330
Differential evolution (DE) is a simple and powerful population-based search algorithm, successfully used in various scientific
and engineering fields. However, DE is not free from the problems of stagnation and premature convergence. Hence, designing
more effective search strategies to enhance the performance of DE is one of the most salient and active topics. This paper
proposes a new method, called learning-enhanced DE (LeDE) that promotes individuals to exchange information systematically.
Distinct from the existing DE variants, LeDE adopts a novel learning strategy, namely clustering-based learning strategy (CLS).
In CLS, there are two levels of learning strategies, intra-cluster learning strategy and inter-cluster learning strategy.
They are adopted for exchanging information within the same cluster and between different clusters, respectively. Experimental
studies over 23 benchmark functions show that LeDE significantly outperforms the conventional DE. Compared with other clustering-based
DE algorithms, LeDE can obtain better solutions. In addition, LeDE is also shown to be significantly better than or at least
comparable to several state-of-art DE variants as well as some other evolutionary algorithms. 相似文献