Above are papers related to the following paper: [Paper_ID] Paper #1129494. 	[Title] Extended Kalman filter in recurrent neural network training and pruning [Abstract] Recently, extended Kalman filter (EKF) based training has been demonstrated to be effective in neural network training. However, its conjunction with pruning methods such as weight decay and optimal brain damage (OBD) has not yet been studied. In this paper, we will elucidate the method of EKF training and propose a pruning method which is based on the results obtained by EKF training. These combined training pruning method is applied to a time series prediction problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #604073. 	[Title] Pruning with generalization based weight saliencies: flOBD, flOBS [Abstract] The purpose of most architecture optimization schemes is to improve generalization. In this presentation we suggest to estimate the weight saliency as the associated change in generalization error if the weight is pruned. We detail the implementation of both an O(N )-storage scheme extending OBD, as well as an O(N 2 ) scheme extending OBS. We illustrate the viability of the approach on pre diction of a chaotic time series. Please summarise all the given information.

Paper #1129494 has the title `Extended Kalman filter in recurrent neural network training and pruning  
` and the abstract `Recently, extended Kalman filter (EKF) based training has been demonstrated to be effective in neural network training. However, its conjunction with pruning methods such as weight decay and optimal brain damage (OBD) has not yet been studied. In this paper, we will elucidate the method of EKF training and propose a pruning method which is based on the results obtained by EKF training. These combined training pruning method is applied to a time series prediction problem.
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Question: Which category should Paper #1129494 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1129494 has the title `Extended Kalman filter in recurrent neural network training and pruning  
` and the abstract `Recently, extended Kalman filter (EKF) based training has been demonstrated to be effective in neural network training. However, its conjunction with pruning methods such as weight decay and optimal brain damage (OBD) has not yet been studied. In this paper, we will elucidate the method of EKF training and propose a pruning method which is based on the results obtained by EKF training. These combined training pruning method is applied to a time series prediction problem.
`.
Question: Which category should Paper #1129494 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks