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ABSTRACT: The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.
Neural Computation 03/2013; · 1.88 Impact Factor
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ABSTRACT: We use a supervised multi-spike learning algorithm for spiking neural networks (SNNs) with temporal encoding to simulate the learning mechanism of biological neurons in which the SNN output spike trains are encoded by firing times. We first analyze why existing gradient-descent-based learning methods for SNNs have difficulty in achieving multi-spike learning. We then propose a new multi-spike learning method for SNNs based on gradient descent that solves the problems of error function construction and interference among multiple output spikes during learning. The method could be widely applied to single spiking neurons to learn desired output spike trains and to multilayer SNNs to solve classification problems. By overcoming learning interference among multiple spikes, our method has high learning accuracy when there are a relatively large number of output spikes in need of learning. We also develop an output encoding strategy with respect to multiple spikes for classification problems. This effectively improves the classification accuracy of multi-spike learning compared to that of single-spike learning.
Neural networks: the official journal of the International Neural Network Society 02/2013; 43C:99-113. · 1.88 Impact Factor
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ABSTRACT: In information retrieval, data fusion has been investigated by many researchers. Previous investigation and experimentation
demonstrate that the linear combination method is an effective data fusion method for combining multiple information retrieval
results. One advantage is its flexibility since different weights can be assigned to different component systems so as to
obtain better fusion results. However, how to obtain suitable weights for all the component retrieval systems is still an
open problem.
In this paper, we use the multiple linear regression technique to obtain optimum weights for all involved component systems.
Optimum is in the least squares sense that minimize the difference between the estimated scores of all documents by linear
combination and the judged scores of those documents. Our experiments with four groups of runs submitted to TREC show that
the linear combination method with such weights steadily outperforms the best component system and other major data fusion
methods such as CombSum, CombMNZ, and the linear combination method with performance level/performance square weighting schemas
by large margins.
08/2011: pages 219-233;
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Advances in Databases - 28th British National Conference on Databases, BNCOD 28, Manchester, UK, July 12-14, 2011, Revised Selected Papers; 01/2011
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Database and Expert Systems Applications - 22nd International Conference, DEXA 2011, Toulouse, France, August 29 - September 2, 2011, Proceedings, Part II; 01/2011
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ABSTRACT: In this paper we present a new data fusion method in information retrieval, which uses ranking information of resultant documents. Our method is based on the modelling of rank-probability of relevance of documents in resultant document list using logarithmic models. The proposed method is more effective than other data fusion methods which also use ranking information, and is as effective as some data fusion methods which rely on reliable scoring information.
Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on; 09/2010
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SCIENCE CHINA Information Sciences. 01/2010; 53:2399-2414.
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International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, Spain, 18-23 July, 2010; 01/2010
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Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA, 11-14 October 2009; 01/2009
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Foundations of Intelligent Systems, 17th International Symposium, ISMIS 2008, Toronto, Canada, May 20-23, 2008, Proceedings; 01/2008
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Intelligent Data Engineering and Automated Learning - IDEAL 2008, 9th International Conference, Daejeon, South Korea, November 2-5, 2008, Proceedings; 01/2008
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Proceedings of the International Joint Conference on Neural Networks, IJCNN 2007, Celebrating 20 years of neural networks, Orlando, Florida, USA, August 12-17, 2007; 01/2007
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ABSTRACT: In information retrieval systems and digital libraries, result presentation is a very important aspect. In this paper, we
demonstrate that only a ranked list of documents, thought commonly used by many retrieval systems and digital libraries, is
not the best way of presenting retrieval results. We believe, in many situations, an estimated relevance probability score
or an estimated relevance score should be provided for every retrieved document by the information retrieval system/digital
library. With such information, the usability of the retrieval result can be improved, and the Euclidean distance can be used
as a very good system-oriented measure for the effectiveness of retrieval results. The relationship between the Euclidean
distance and some ranking-based measures are also investigated.
01/1970: pages 125-136;
