Above are papers related to the following paper: [Paper_ID] Paper #853155. 	[Title] LU TP 91-25 Mass Reconstruction with a Neural Network [Abstract] A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W ! q q, where W -bosons are produced in pp reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using "intelligent" variables in instances when the amount of training instances is limited. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1140289. 	[Title] LU TP 93-24 Predicting System Loads with Artificial Neural [Abstract] Networks Methods and Results from Abstract: We devise a feed-forward Artificial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by "The Great Energy Predictor Shootout The First Building Data Analysis and Prediction Competition" [1]. Key ingredients in our approach are a method (ffi -test) for determining relevant inputs and the Multilayer Perceptron. These methods are briefly reviewed together with comments on alternative schemes like fitting to polynomials and the use of recurrent networks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19621. 	[Title] References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature [Abstract] 2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Internal Representations by Error Propagation", in D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1), MIT Press (1986). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #853114. 	[Title] Using Neural Networks to Identify Jets [Abstract] A neural network method for identifying the ancestor of a hadron jet is presented. The idea is to find an efficient mapping between certain observed hadronic kinematical variables and the quark/gluon identity. This is done with a neuronic expansion in terms of a network of sigmoidal functions using a gradient descent procedure, where the errors are back-propagated through the network. With this method we are able to separate gluon from quark jets originating from Monte Carlo generated e + e events with ~ 85% accuracy. The result is independent on the MC model used. This approach for isolating the gluon jet is then used to study the so-called string effect. In addition, heavy quarks (b and c) in e + e reactions can be identified on the 50% level by just observing the hadrons. In particular we are able to separate b-quarks with an efficiency and purity, which is comparable with what is expected from vertex detectors. We also speculate on how the neural network method can be used to disentangle different hadronization schemes by compressing the dimensionality of the state space of hadrons. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #853115. 	[Title] LU TP 90-3 Finding Gluon Jets with a Neural Trigger [Abstract] Using a neural network classifier we are able to separate gluon from quark jets originating from Monte Carlo generated e + e events with 85 90% accuracy. PACS numbers: 13.65.+i, 12.38Qk, 13.87.Fh Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #853116. 	[Title] LU TP 91-4 Self-organizing Networks for Extracting Jet Features [Abstract] Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b,c and light quarks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #853118. 	[Title] LU TP 93-13 On Langevin Updating in Multilayer Perceptrons [Abstract] The Langevin updating rule, in which noise is added to the weights during learning, is presented and shown to improve learning on problems with initially ill-conditioned Hessians. This is particularly important for multilayer perceptrons with many hidden layers, that often have ill-conditioned Hessians. In addition, Manhattan updating is shown to have a similar effect. Please summarise all the given information.

Paper #853155 has the title `LU TP 91-25 Mass Reconstruction with a Neural Network  
` and the abstract `A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W ! q q, where W -bosons are produced in pp reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using "intelligent" variables in instances when the amount of training instances is limited. 
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Question: Which category should Paper #853155 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #853155 has the title `LU TP 91-25 Mass Reconstruction with a Neural Network  
` and the abstract `A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W ! q q, where W -bosons are produced in pp reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using "intelligent" variables in instances when the amount of training instances is limited. 
`.
Question: Which category should Paper #853155 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