Above are papers related to the following paper: [Paper_ID] Paper #1120880. 	[Title] Robust Interpretation of Neural-Network Models [Abstract] Artificial Neural Network seem very promising for regression and classification, especially for large covariate spaces. These methods represent a non-linear function as a composition of low dimensional ridge functions and therefore appear to be less sensitive to the dimensionality of the covariate space. However, due to non uniqueness of a global minimum and the existence of (possibly) many local minima, the model revealed by the network is non stable. We introduce a method to interpret neural network results which uses novel robustification techniques. This results in a robust interpretation of the model employed by the network. Simulated data from known models is used to demonstrate the interpretability results and to demonstrate the effects of different regularization methods on the robustness of the model. Graphical methods are introduced to present the interpretation results. We further demonstrate how interaction between covariates can be revealed. From this study we conclude that the interpretation method works well, but that NN models may sometimes be misinterpreted, especially if the approximations to the true model are less robust. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7041. 	[Title] Bootstrapping with Noise: An Effective Regularization Technique [Abstract] Bootstrap samples with noise are shown to be an effective smoothness and capacity control technique for training feed-forward networks and for other statistical methods such as generalized additive models. It is shown that noisy bootstrap performs best in conjunction with weight decay regularization and ensemble averaging. The two-spiral problem, a highly non-linear noise-free data, is used to demonstrate these findings. The combination of noisy bootstrap and ensemble averaging is also shown useful for generalized additive modeling, and is also demonstrated on the well known Cleveland Heart Data [7]. Please summarise all the given information.

Paper #1120880 has the title `Robust Interpretation of Neural-Network Models  
` and the abstract `Artificial Neural Network seem very promising for regression and classification, especially for large covariate spaces. These methods represent a non-linear function as a composition of low dimensional ridge functions and therefore appear to be less sensitive to the dimensionality of the covariate space. However, due to non uniqueness of a global minimum and the existence of (possibly) many local minima, the model revealed by the network is non stable. We introduce a method to interpret neural network results which uses novel robustification techniques. This results in a robust interpretation of the model employed by the network. Simulated data from known models is used to demonstrate the interpretability results and to demonstrate the effects of different regularization methods on the robustness of the model. Graphical methods are introduced to present the interpretation results. We further demonstrate how interaction between covariates can be revealed. From this study we conclude that the interpretation method works well, but that NN models may sometimes be misinterpreted, especially if the approximations to the true model are less robust. 
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Question: Which category should Paper #1120880 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1120880 has the title `Robust Interpretation of Neural-Network Models  
` and the abstract `Artificial Neural Network seem very promising for regression and classification, especially for large covariate spaces. These methods represent a non-linear function as a composition of low dimensional ridge functions and therefore appear to be less sensitive to the dimensionality of the covariate space. However, due to non uniqueness of a global minimum and the existence of (possibly) many local minima, the model revealed by the network is non stable. We introduce a method to interpret neural network results which uses novel robustification techniques. This results in a robust interpretation of the model employed by the network. Simulated data from known models is used to demonstrate the interpretability results and to demonstrate the effects of different regularization methods on the robustness of the model. Graphical methods are introduced to present the interpretation results. We further demonstrate how interaction between covariates can be revealed. From this study we conclude that the interpretation method works well, but that NN models may sometimes be misinterpreted, especially if the approximations to the true model are less robust. 
`.
Question: Which category should Paper #1120880 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