Results 221 to 230 of about 297,748 (266)

Improved filtering for the QSIM algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993
A source of spurious predictions inside the qualitative simulation algorithm QSIM proposed by B. Kuiper (1986) is identified. The proposed solution involves the use of interval corresponding values for filtering inconsistent states and does not require any additions or restrictions in the input set, unlike the other approaches to the spurious ...
A. C. Cem Say, Selahattin Kuru
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Improving Collaborative Filtering Algorithms

2016 12th International Conference on Semantics, Knowledge and Grids (SKG), 2016
In this paper, we propose a new recommender algorithm based on Slope One algorithm and new similarity measurements. We incorporate additional sources of information about the users to relieve the cold start problem. Users generate a large number of interactions while browsing a website.
Firas Ben Kharrat, Aymen Elkhlifi, Rim Faiz   +2 more
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The filtered error LMS Algorithm

ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
In this paper, we show that the LMS algorithm fails to converge to an optimal solution in the presence of nonstationary noise. To overcome this problem, we introduce the filtered error LMS (FELMS) algorithm. The FELMS algorithm consists of two interconnected adaptive processes. The first process filters the error used by the other.
S. Shaffer, C. S. Williams
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A filtering algorithm for Web caches

Computer Networks and ISDN Systems, 1998
Abstract Most proxies use an aggressive caching policy: they cache all objects that can be cached. This policy has the advantage that given sufficient disk space it maximizes the hit rate. However, maximizing hit rate does not necessarily maximize proxy cache performance.
Michal Kurcewicz, Wojtek Sylwestrzak, Adam Wierzbicki   +2 more
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On Little's Digital Filtering Algorithm

IEEE Transactions on Computers, 1977
It is shown that Little's algorithm for high-speed digital filters can be applied to data represented in two's complement form. It is not necessary to use the more complex "biased" form or negative base representation.
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An iterative algorithm for inverse filtering

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
If the Z transform of a filter has some zero points which are on or very close to the unit circle, the inverse filter will extend for many significant terms instead of vanishing in a few terms, and a tremendous truncation error will result. An iterative algorithm has been found to handle this zero-point problem.
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Recursive inverse adaptive filtering algorithm

Digital Signal Processing, 2009
In this paper, a new FIR adaptive filtering algorithm is proposed. The approach uses a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation that leads to an improved performance. Convergence analysis of the algorithm has been presented. Simulation results show that the algorithm performs better
Mohammad Shukri Ahmad, Osman Kukrer, Aykut Hocanin   +2 more
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Design of fuzzy filters by genetic algorithms

Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 2002
In this paper a new strategy for the design of fuzzy filters is proposed through the use of a genetic optimisation algorithm (GAs). The innovation of the proposed approach lies in the possibility to design fuzzy filters directly in the frequency domain.
CAPONETTO, Riccardo, L. Fortuna, C. Vinci   +2 more
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Efficient Algorithms for the Line-SIAC Filter

Journal of Scientific Computing, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ashok Jallepalli, Robert M. Kirby
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A review of recursive filtering algorithms

Proceedings of the November 16-18, 1971, fall joint computer conference on - AFIPS '71 (Fall), 1971
The recursive filtering theory introduced scarcely a decade ago by Kalman and Kalman and Bucy has been widely hailed as a major development in data processing, perhaps as important as the work of Weiner on linear filtering.
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