Above are papers related to the following paper: [Paper_ID] Paper #1132443. 	[Title] The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING [Abstract] This paper introduces the idea of clearning, of simultaneously cleaning data and learning the underlying structure. The cleaning step can be viewed as top-down processing (the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). After discussing the statistical foundation of the proposed method from a maximum likelihood perspective, we apply clearning to a notoriously hard problem where benchmark performances are very well known: the prediction of foreign exchange rates. On the difficult 1993-1994 test period, clearning in conjunction with pruning yields an annualized return between 35 and 40% (out-of-sample), significantly better than an otherwise identical network trained without cleaning. The network was started with 69 inputs and 15 hidden units and ended up with only 39 non-zero weights between inputs and hidden units. The resulting ultra-sparse final architectures obtained with clearning and pruning are immune against overfitting, even on very noisy problems since the cleaned data allow for a simpler model. Apart from the very competitive performance, clearning gives insight into the data: we show how to estimate the overall signal-to-noise ratio of each input variable, and we show that error estimates for each pattern can be used to detect and remove outliers, and to replace missing or corrupted data by cleaned values. Clearning can be used in any nonlinear regression or classification problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132418. 	[Title] NONPARAMETRIC SELECTION OF INPUT VARIABLES FOR CONNECTIONIST LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #671269. 	[Title] Predicting Conditional Probability Distributions: A Connectionist Approach [Abstract] Most traditional prediction techniques deliver the mean of the probability distribution (a single point). For multimodal processes, instead of predicting the mean of the probability distribution, it is important to predict the full distribution. This article presents a new connectionist method to predict the conditional probability distribution in response to an input. The main idea is to transform the problem from a regression to a classification problem. The conditional probability distribution network can perform both direct predictions and iterated predictions, a task which is specific for time series problems. We compare our method to fuzzy logic and discuss important differences, and also demonstrate the architecture on two time series. The first is the benchmark laser series used in the Santa Fe competition, a deterministic chaotic system. The second is a time series from a Markov process which exhibits structure on two time scales. The network produces multimodal predictions for this series. We compare the predictions of the network with a nearest-neighbor predictor and find that the conditional probability network is more than twice as likely a model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #672070. 	[Title] Evaluating Neural Network Predictors by Bootstrapping [Abstract] We present a new method, inspired by the bootstrap, whose goal it is to determine the quality and reliability of a neural network predictor. Our method leads to more robust forecasting along with a large amount of statistical information on forecast performance that we exploit. We exhibit the method in the context of multi-variate time series prediction on financial data from the New York Stock Exchange. It turns out that the variation due to different resamplings (i.e., splits between training, cross-validation, and test sets) is significantly larger than the variation due to different network conditions (such as architecture and initial weights). Furthermore, this method allows us to forecast a probability distribution, as opposed to the traditional case of just a single value at each time step. We demonstrate this on a strictly held-out test set that includes the 1987 stock market crash. We also compare the performance of the class of neural networks to identically bootstrapped linear models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #24966. 	[Title] A Practical Bayesian Framework for Backprop Networks [Abstract] A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability and the Bayesian evidence is obtained. This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #672071. 	[Title] Predictions with Confidence Intervals (Local Error Bars) [Abstract] We present a new method for obtaining local error bars, i.e., estimates of the confidence in the predicted value that depend on the input. We approach this problem of nonlinear regression in a maximum likelihood framework. We demonstrate our technique first on computer generated data with locally varying, normally distributed target noise. We then apply it to the laser data from the Santa Fe Time Series Competition. Finally, we extend the technique to estimate error bars for iterated predictions, and apply it to the exact competition task where it gives the best performance to date. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154123. 	[Title]  [Abstract] Most connectionist modeling assumes noise-free inputs. This assumption is often violated. This paper introduces the idea of clearning, of simultaneously cleaning the data and learning the underlying structure. The cleaning step can be viewed as top-down processing (where the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). Clearning is used in conjunction with standard pruning. This paper discusses the statistical foundation of clearning, gives an interpretation in terms of a mechanical model, describes how to obtain both point predictions and conditional densities for the output, and shows how the resulting model can be used to discover properties of the data otherwise not accessible (such as the signal-to-noise ratio of the inputs). This paper uses clearning to predict foreign exchange rates, a noisy time series problem with well-known benchmark performances. On the out-of-sample 1993-1994 test period, clearning obtains an annualized return on investment above 30%, significantly better than an otherwise identical network. The final ultra-sparse network with 36 remaining non-zero input-to-hidden weights (of the 1035 initial weights between 69 inputs and 15 hidden units) is very robust against overfitting. This small network also lends itself to interpretation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154124. 	[Title]  [Abstract] Most connectionist modeling assumes noise-free inputs. This assumption is often violated. This paper introduces the idea of clearning, of simultaneously cleaning the data and learning the underlying structure. The cleaning step can be viewed as top-down processing (where the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). Clearning is used in conjunction with standard pruning. This paper discusses the statistical foundation of clearning, gives an interpretation in terms of a mechanical model, describes how to obtain both point predictions and conditional densities for the output, and shows how the resulting model can be used to discover properties of the data otherwise not accessible (such as the signal-to-noise ratio of the inputs). This paper uses clearning to predict foreign exchange rates, a noisy time series problem with well-known benchmark performances. On the out-of-sample 1993-1994 test period, clearning obtains an annualized return on investment above 30%, significantly better than an otherwise identical network. The final ultra-sparse network with 36 remaining non-zero input-to-hidden weights (of the 1035 initial weights between 69 inputs and 15 hidden units) is very robust against overfitting. This small network also lends itself to interpretation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132461. 	[Title] Avoiding overfitting by locally matching the noise level of the data gating network discovers the [Abstract] When trying to forecast the future behavior of a real-world system, two of the key problems are nonstationarity of the process (e.g., regime switching) and overfitting of the model (particularly serious for noisy processes). This articles shows how gated experts can point to solutions to these problems. The architecture, also called society of experts and mixture of experts consists of a (nonlinear) gating network and several (nonlinear) competing experts. Each expert learns a conditional mean (as usual), but each expert also has its own adaptive width. The gating network learns to assign a probability to each expert that depends on the input. This article first discusses the assumptions underlying this architecture and derives the weight update rules. It then evaluates the performance of gated experts in comparison to that of single networks, as well as to networks with two outputs, one predicting the mean, the other one the local error bar. This article also investigates the ability of gated experts to discover and characterize underlying the regimes. The results are: * there is significantly less overfitting compared to single nets, for two reasons: only subsets of the potential inputs are given to the experts and gating network (less of a curse of dimensionality), and the experts learn to match their variances to the (local) noise levels, thus only learning as This article focuses on the architecture and the overfitting problem. Applications to a computer-generated toy problem and the laser data from Santa Fe Competition are given in [Mangeas and Weigend, 1995], and the application to the real-world problem of predicting the electricity demand of France are given in [Mangeas et al., 1995]. much as the data support. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6130. 	[Title] ``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression [Abstract] We present a new method for obtaining local error bars for nonlinear regression, i.e., estimates of the confidence in predicted values that depend on the input. We approach this problem by applying a maximum-likelihood framework to an assumed distribution of errors. We demonstrate our method first on computer-generated data with locally varying, normally distributed target noise. We then apply it to laser data from the Santa Fe Time Series Competition where the underlying system noise is known quantization error and the error bars give local estimates of model misspecification. In both cases, the method also provides a weighted-regression effect that improves generalization performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40151. 	[Title] Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation [Abstract] Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as gradient descent are helpful in searching through the space of models, but problems such as local minima, and more importantly, lack of a distance metric between various models reduce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly discarding bad models, and concentrating the computational effort at differentiating between the better ones. This paper focuses on the special case of leave-one-out cross validation applied to memory-based learning algorithms, but we also argue that it is applicable to any class of model selection problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #641976. 	[Title] A comparison of some error estimates for neural network models  Summary [Abstract] We discuss a number of methods for estimating the standard error of predicted values from a multi-layer perceptron. These methods include the delta method based on the Hessian, bootstrap estimators, and the "sandwich" estimator. The methods are described and compared in a number of examples. We find that the bootstrap methods perform best, partly because they capture variability due to the choice of starting weights. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #671293. 	[Title] Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation [Abstract] In learning problems where a connectionist network is trained with a finite sized training set, better generalization performance is often obtained when unneeded weights in the network are eliminated. One source of unneeded weights comes from the inclusion of input variables that provide little information about the output variables. We propose a method for identifying and eliminating these input variables. The method first determines the relationship between input and output variables using nonparametric density estimation and then measures the relevance of input variables using the information theoretic concept of mutual information. We present results from our method on a simple toy problem and a nonlinear time series. Please summarise all the given information.

Paper #1132443 has the title `The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING  
` and the abstract `This paper introduces the idea of clearning, of simultaneously cleaning data and learning the underlying structure. The cleaning step can be viewed as top-down processing (the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). After discussing the statistical foundation of the proposed method from a maximum likelihood perspective, we apply clearning to a notoriously hard problem where benchmark performances are very well known: the prediction of foreign exchange rates. On the difficult 1993-1994 test period, clearning in conjunction with pruning yields an annualized return between 35 and 40% (out-of-sample), significantly better than an otherwise identical network trained without cleaning. The network was started with 69 inputs and 15 hidden units and ended up with only 39 non-zero weights between inputs and hidden units. The resulting ultra-sparse final architectures obtained with clearning and pruning are immune against overfitting, even on very noisy problems since the cleaned data allow for a simpler model. Apart from the very competitive performance, clearning gives insight into the data: we show how to estimate the overall signal-to-noise ratio of each input variable, and we show that error estimates for each pattern can be used to detect and remove outliers, and to replace missing or corrupted data by cleaned values. Clearning can be used in any nonlinear regression or classification problem.
`.
Question: Which category should Paper #1132443 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1132443 has the title `The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING  
` and the abstract `This paper introduces the idea of clearning, of simultaneously cleaning data and learning the underlying structure. The cleaning step can be viewed as top-down processing (the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). After discussing the statistical foundation of the proposed method from a maximum likelihood perspective, we apply clearning to a notoriously hard problem where benchmark performances are very well known: the prediction of foreign exchange rates. On the difficult 1993-1994 test period, clearning in conjunction with pruning yields an annualized return between 35 and 40% (out-of-sample), significantly better than an otherwise identical network trained without cleaning. The network was started with 69 inputs and 15 hidden units and ended up with only 39 non-zero weights between inputs and hidden units. The resulting ultra-sparse final architectures obtained with clearning and pruning are immune against overfitting, even on very noisy problems since the cleaned data allow for a simpler model. Apart from the very competitive performance, clearning gives insight into the data: we show how to estimate the overall signal-to-noise ratio of each input variable, and we show that error estimates for each pattern can be used to detect and remove outliers, and to replace missing or corrupted data by cleaned values. Clearning can be used in any nonlinear regression or classification problem.
`.
Question: Which category should Paper #1132443 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