The Experts below are selected from a list of 62331 Experts worldwide ranked by ideXlab platform
Zicheng Huang - One of the best experts on this subject based on the ideXlab platform.
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RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation
2010 IEEE International Conference on Web Services, 2010Co-Authors: Xi Chen, Zicheng HuangAbstract:Several approaches to web service selection and recommendation via collaborative Filtering have been studied, but seldom have these studies considered the difference between web service recommendation and product recommendation used in e-commerce sites. In this paper, we present RegionKNN, a novel hybrid collaborative Filtering Algorithm that is designed for large scale web service recommendation. Different from other approaches, this method employs the characteristics of QoS by building an efficient region model. Based on this model, web service recommendations will be generated quickly by using modified memory-based collaborative Filtering Algorithm. Experimental results demonstrate that apart from being highly scalable, RegionKNN provides considerable improvement on the recommendation accuracy by comparing with other well-known collaborative Filtering Algorithms.
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ICWS - RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation
2010 IEEE International Conference on Web Services, 2010Co-Authors: Xi Chen, Zicheng HuangAbstract:Several approaches to web service selection and recommendation via collaborative Filtering have been studied, but seldom have these studies considered the difference between web service recommendation and product recommendation used in e-commerce sites. In this paper, we present RegionKNN, a novel hybrid collaborative Filtering Algorithm that is designed for large scale web service recommendation. Different from other approaches, this method employs the characteristics of QoS by building an efficient region model. Based on this model, web service recommendations will be generated quickly by using modified memory-based collaborative Filtering Algorithm. Experimental results demonstrate that apart from being highly scalable, RegionKNN provides considerable improvement on the recommendation accuracy by comparing with other well-known collaborative Filtering Algorithms.
A Antoniou - One of the best experts on this subject based on the ideXlab platform.
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robust set membership affine projection adaptive Filtering Algorithm
IEEE Transactions on Signal Processing, 2012Co-Authors: Md Zulfiquar Ali Bhotto, A AntoniouAbstract:An improved set-membership affine-projection (AP) adaptive-Filtering Algorithm is proposed. The new Algorithm uses two error bounds that are estimated during the learning phase and by this means significantly reduced steady-state misalignment is achieved as compared to those in the conventional AP and set-membership AP Algorithms while achieving similar convergence speed and re-adaptation capability. In addition, the proposed Algorithm offers robust performance with respect to the error bound, projection order, impulsive-noise interference, and in tracking abrupt changes in the underlying system. These features of the proposed Algorithm are demonstrated through extensive simulation results in system-identification and echo-cancellation applications.
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robust recursive least squares adaptive Filtering Algorithm for impulsive noise environments
IEEE Signal Processing Letters, 2011Co-Authors: Md Zulfiquar Ali Bhotto, A AntoniouAbstract:A new robust recursive least-squares (RLS) adaptive Filtering Algorithm that uses a priori error-dependent weights is proposed. Robustness against impulsive noise is achieved by choosing the weights on the basis of the L1 norms of the crosscorrelation vector and the input-signal autocorrelation matrix. The proposed Algorithm also uses a variable forgetting factor that leads to fast tracking. Simulation results show that the proposed Algorithm offers improved robustness as well as better tracking compared to the conventional RLS and recursive least-M estimate adaptation Algorithms.
Xi Chen - One of the best experts on this subject based on the ideXlab platform.
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RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation
2010 IEEE International Conference on Web Services, 2010Co-Authors: Xi Chen, Zicheng HuangAbstract:Several approaches to web service selection and recommendation via collaborative Filtering have been studied, but seldom have these studies considered the difference between web service recommendation and product recommendation used in e-commerce sites. In this paper, we present RegionKNN, a novel hybrid collaborative Filtering Algorithm that is designed for large scale web service recommendation. Different from other approaches, this method employs the characteristics of QoS by building an efficient region model. Based on this model, web service recommendations will be generated quickly by using modified memory-based collaborative Filtering Algorithm. Experimental results demonstrate that apart from being highly scalable, RegionKNN provides considerable improvement on the recommendation accuracy by comparing with other well-known collaborative Filtering Algorithms.
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ICWS - RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation
2010 IEEE International Conference on Web Services, 2010Co-Authors: Xi Chen, Zicheng HuangAbstract:Several approaches to web service selection and recommendation via collaborative Filtering have been studied, but seldom have these studies considered the difference between web service recommendation and product recommendation used in e-commerce sites. In this paper, we present RegionKNN, a novel hybrid collaborative Filtering Algorithm that is designed for large scale web service recommendation. Different from other approaches, this method employs the characteristics of QoS by building an efficient region model. Based on this model, web service recommendations will be generated quickly by using modified memory-based collaborative Filtering Algorithm. Experimental results demonstrate that apart from being highly scalable, RegionKNN provides considerable improvement on the recommendation accuracy by comparing with other well-known collaborative Filtering Algorithms.
Md Zulfiquar Ali Bhotto - One of the best experts on this subject based on the ideXlab platform.
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robust set membership affine projection adaptive Filtering Algorithm
IEEE Transactions on Signal Processing, 2012Co-Authors: Md Zulfiquar Ali Bhotto, A AntoniouAbstract:An improved set-membership affine-projection (AP) adaptive-Filtering Algorithm is proposed. The new Algorithm uses two error bounds that are estimated during the learning phase and by this means significantly reduced steady-state misalignment is achieved as compared to those in the conventional AP and set-membership AP Algorithms while achieving similar convergence speed and re-adaptation capability. In addition, the proposed Algorithm offers robust performance with respect to the error bound, projection order, impulsive-noise interference, and in tracking abrupt changes in the underlying system. These features of the proposed Algorithm are demonstrated through extensive simulation results in system-identification and echo-cancellation applications.
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robust recursive least squares adaptive Filtering Algorithm for impulsive noise environments
IEEE Signal Processing Letters, 2011Co-Authors: Md Zulfiquar Ali Bhotto, A AntoniouAbstract:A new robust recursive least-squares (RLS) adaptive Filtering Algorithm that uses a priori error-dependent weights is proposed. Robustness against impulsive noise is achieved by choosing the weights on the basis of the L1 norms of the crosscorrelation vector and the input-signal autocorrelation matrix. The proposed Algorithm also uses a variable forgetting factor that leads to fast tracking. Simulation results show that the proposed Algorithm offers improved robustness as well as better tracking compared to the conventional RLS and recursive least-M estimate adaptation Algorithms.
Garrick Orchard - One of the best experts on this subject based on the ideXlab platform.
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a noise Filtering Algorithm for event based asynchronous change detection image sensors on truenorth and its implementation on truenorth
Frontiers in Neuroscience, 2018Co-Authors: Vandana Padala, Arindam Basu, Garrick OrchardAbstract:Asynchronous event-based sensors, or “silicon retinae,” are a new class of vision sensors inspired by biological vision systems. The output of these sensors often contains a significant number of noise events along with the signal. Filtering these noise events is a common preprocessing step before using the data for tasks such as tracking and classification. This paper presents a novel spiking neural network-based approach to Filtering noise events from data captured by an Asynchronous Time-based Image Sensor on a neuromorphic processor, the IBM TrueNorth Neurosynaptic System. The significant contribution of this work is that it demonstrates our proposed Filtering Algorithm not only outperforms the traditional nearest neighbour neighbour noise filter in achieving higher signal to noise ratio ( ~10 dB higher) and retaining the events related to signal (~3X more). In addition, for some parameters, it can also generate some of the missing events in the spatial neighbourhood of the signal for all classes of moving objects in the data which are unattainable using the nearest neighbour filter.
