Median filters and the more general order-statistic-based filters have proven very useful in filtering signals corrupted by noise with heavier tails than the Gaussian, in filtering signals with jumps or edges, or in s...
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Median filters and the more general order-statistic-based filters have proven very useful in filtering signals corrupted by noise with heavier tails than the Gaussian, in filtering signals with jumps or edges, or in situations where both occur. The author presents two simple recursive definitions for the kth order statistic taken from n samples. From these recursive expressions, some simple inequalities satisfied by order statistics are presented. Formulas for computing distributions of order statistics are derived. Finally, a series of VLSI implementations for computing both a fixed-order statistic (e.g. the median) or all-order statistics is presented.< >
The authors propose a simple and efficient technique to improve the tracking capability of numerically stable fast, least-squares algorithms. The forgetting factor for the adaptation of the filtering part is implicitl...
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The authors propose a simple and efficient technique to improve the tracking capability of numerically stable fast, least-squares algorithms. The forgetting factor for the adaptation of the filtering part is implicitly modified, while the forgetting factor in the prediction part is kept to a value that ensures the numerical stability. A theoretical analysis of the modified algorithm is presented. Simulation results on a practical example (acoustic echo cancellation) show the efficiency of the proposed technique.< >
Recommendation systems are widely used in various industries and are seen as one of the effective methods in reducing information overload. This paper selected four common algorithms for implementation and evaluation ...
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Recommendation systems are widely used in various industries and are seen as one of the effective methods in reducing information overload. This paper selected four common algorithms for implementation and evaluation among many recommendation algorithms. Two traditional collaborative filtering algorithms - User Collaboration Filter and Item-based Collaborative filtering; most popular algorithms in the recommendation field - Matrix Factorization; and Neural Collaborative filtering - the algorithm based on Matrix Factorization and combined with neural networks.
This paper discusses adaptive filtering algorithms and proposes a fast algorithm based on vector plots analysis, that is different from the previous adaptive filtering algorithms. By introducing approaches of mathemat...
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ISBN:
(纸本)0780386531
This paper discusses adaptive filtering algorithms and proposes a fast algorithm based on vector plots analysis, that is different from the previous adaptive filtering algorithms. By introducing approaches of mathematical geometrical analysis to study adaptive filtering, the paper inquires into vector plots structure of least mean squares (LMS) algorithm and geometrical feature of algorithm convergence and seeks effective fast algorithm on the basis of geometrical analysis. Numerical simulations are given to show the efficiency and superiority of the new algorithm.
Web filtering based on user's demand has witnessed a booming interest due to the development of Internet In the research community the dominant approach to this problem is based on machine learning algorithms. Web...
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ISBN:
(纸本)0780384032
Web filtering based on user's demand has witnessed a booming interest due to the development of Internet In the research community the dominant approach to this problem is based on machine learning algorithms. Web filtering is an inductive process which automatically builds a filter by learning from a set of pre-assigned document and the description of user's interest, and then uses it to assign unfiltered Web pages. This survey compares four main machine learning algorithms (decision tree, rule induction, Bayesian algorithm and support vector machines) on Chinese web pages set of their filtering effectiveness and computer resources consumed, focusing on the influence of feature set size and training set size. It induces that support vector machines earn high score in Chinese Web filtering applications.
Along with the development of the Internet, how to manage and control network information resources effectively has become a hot research. In this paper, we discussed key technologies of network information filtering:...
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Along with the development of the Internet, how to manage and control network information resources effectively has become a hot research. In this paper, we discussed key technologies of network information filtering: feature selection and learning algorithm. Based on feature subset generated by feature selection would affect the filtering accuracy, we proposed an improved feature selection method CHIIDF, using this method to remove redundant features, then using annealing genetic algorithm to learn and to get user profile. Finally, we developed the system of network information filtering and analyzed the data that achieved good results.
Collaborative filtering is becoming a popular technique for reducing information overload. Many algorithms have been proposed for collaborative filtering. The performance of a recommended system during the startup sta...
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ISBN:
(纸本)0780388127
Collaborative filtering is becoming a popular technique for reducing information overload. Many algorithms have been proposed for collaborative filtering. The performance of a recommended system during the startup stage is crucial to the system. If recommendation is close to what an user really want, the user would be glad to use the system later, else he may never make use of it again. In this paper, we compare the performance results of four collaborative filtering algorithms applied in the startup stage of recommendation. We evaluate these algorithms using three publicly available datasets. Our experiments results show that Pearson and STIN1 methods perform better than latent class model (LCM) and singular value decomposition (SVD) methods during the startup stage. The experimental results confirm that the characteristics of datasets keep being an important factor in the performance of methods.
In view of the characteristics of postgraduate education, such as more courses, quicker content updating, flexible examination methods, and multiple courses compounding questions, a random test question extraction alg...
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ISBN:
(数字)9781728196275
ISBN:
(纸本)9781728196282
In view of the characteristics of postgraduate education, such as more courses, quicker content updating, flexible examination methods, and multiple courses compounding questions, a random test question extraction algorithm based on multi-layer filtering model and dynamic probability model is proposed, which strengthens the pertinence of the test question extraction process and improves the efficiency of the algorithm. The results show that the parameters can better approximate the set requirements, the distribution of the problem is reasonable, the versatility is good, and can meet the needs of personalized questions.
This research evaluates a new genetic algorithm for searching multimodal error surfaces. This new technique allows the genetic algorithm to search locally with chromosomes that perform relatively well, while searching...
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This research evaluates a new genetic algorithm for searching multimodal error surfaces. This new technique allows the genetic algorithm to search locally with chromosomes that perform relatively well, while searching globally with the other chromosomes, as opposed to using fixed rates for local and global searches. When only the best solution is important, as in adaptive IIR filtering, the fitness-based exponential genetic algorithm is shown to, on average, outperform the fixed-rate genetic algorithm as well as the fitness-based linear genetic algorithm.
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