Above are papers related to the following paper: [Paper_ID] Paper #1113541. 	[Title] Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm trains a neural network to identify [Abstract] Technical Report UMIACS-TR-97-77 and CS-TR-3843 Abstract Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12359. 	[Title] Pruning Recurrent Neural Networks for Improved Generalization Performance [Abstract] Determining the architecture of a neural network is an important issue for any learning task. For recurrent neural networks no general methods exist that permit the estimation of the number of layers of hidden neurons, the size of layers or the number of weights. We present a simple pruning heuristic which significantly improves the generalization performance of trained recurrent networks. We illustrate this heuristic by training a fully recurrent neural network on positive and negative strings of a regular grammar. We also show that if rules are extracted from networks trained to recognize these strings, that rules extracted after pruning are more consistent with the rules to be learned. This performance improvement is obtained by pruning and retraining the networks. Simulations are shown for training and pruning a recurrent neural net on strings generated by two regular grammars, a randomly-generated 10-state grammar and an 8-state triple parity grammar. Further simulations indicate that this pruning method can gives generalization performance superior to that obtained by training with weight decay. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #242637. 	[Title] A Delay Damage Model Selection Algorithm for NARX Neural Networks [Abstract] Recurrent neural networks have become popular models for system identification and time series prediction. NARX (Nonlinear AutoRegressive models with eXogenous inputs) neural network models are a popular subclass of recurrent networks and have been used in many applications. Though embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show that using intelligent memory order selection through pruning and good initial heuristics significantly improves the generalization and predictive performance of these nonlinear systems on problems as diverse as grammatical inference and time series prediction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #910. 	[Title] Learning networks for face analysis and synthesis [Abstract] This paper presents an overview of the face-related projects in our group. The unifying theme underlying our work is the use of example-based learning methods for both analyzing and synthesizing face images. We label the example face images (and for the problem of face detection, "near miss" faces as well) with descriptive parameters for pose, expression, identity, and face vs. non-face. Then, by using example-based learning techniques, we develop networks for performing analysis tasks such as pose and expression estimation, face recognition, and face detection in cluttered scenes. In addition to these analysis applications, we show how the example-based technique can also be used as a novel method for image synthesis that is for computer graphics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #202639. 	[Title] PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS [Abstract] We investigate the effectiveness of connectionist networks for predicting the future continuation of temporal sequences. The problem of overfitting, particularly serious for short records of noisy data, is addressed by the method of weight-elimination: a term penalizing network complexity is added to the usual cost function in back-propagation. The ultimate goal is prediction accuracy. We analyze two time series. On the benchmark sunspot series, the networks outperform traditional statistical approaches. We show that the network performance does not deteriorate when there are more input units than needed. Weight-elimination also manages to extract some part of the dynamics of the notoriously noisy currency exchange rates and makes the network solution interpretable. Please summarise all the given information.

Paper #1113541 has the title `Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm trains a neural network to identify
` and the abstract `Technical Report UMIACS-TR-97-77 and CS-TR-3843 Abstract 
`.
Question: Which category should Paper #1113541 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1113541 has the title `Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm trains a neural network to identify
` and the abstract `Technical Report UMIACS-TR-97-77 and CS-TR-3843 Abstract 
`.
Question: Which category should Paper #1113541 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