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 #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 #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 #675847. 	[Title] NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION [Abstract] Working Paper IS-97-005, Leonard N. Stern School of Business, New York University. In: Decision Technologies for Financial Engineering (Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets, NNCM-96), pp. 3-22. Edited by A.S.Weigend, Y.S.Abu-Mostafa, and A.-P.N.Refenes. Singapore: World Scientific, 1997. http://www.stern.nyu.edu/~aweigend/Research/Papers/SharpeRatio While many trading strategies are based on price prediction, traders in financial markets are typically interested in risk-adjusted performance such as the Sharpe Ratio, rather than price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can be achieved with this nonlinear approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136397. 	[Title] State Reconstruction for Determining Predictability in Driven Nonlinear Acoustical Systems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #643597. 	[Title] A Nonparametric Bayesian Approach to Modelling Nonlinear Time Series [Abstract] The Bayesian multivariate adaptive regression spline (BMARS) methodology of Denison et al. (1997) is extended to cope with nonlinear time series and financial datasets. The nonlinear time series model is closely related to the adaptive spline threshold autoregressive (ASTAR) method of Lewis and Stevens (1991) while the financial models can be thought of as Bayesian versions of both the generalised and simple autoregressive conditional het-eroscadastic (GARCH and ARCH) models. 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.

Above are papers related to the following paper: [Paper_ID] Paper #69392. 	[Title] Forecasting electricity demand using nonlinear mixture of experts [Abstract] In this paper we study a forecasting model based on mixture of experts for predicting the French electric daily consumption energy. We split the task into two parts. Using mixture of experts, a first model predicts the electricity demand from the exogenous variables (such as temperature and degree of cloud cover) and can be viewed as a nonlinear regression model of mixture of Gaussians. Using a single neural network, a second model predicts the evolution of the residual error of the first one, and can be viewed as an nonlinear autoregression model. We analyze the splitting of the input space generated by the mixture of experts model, and compare the performance to models presently used. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123087. 	[Title] Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference [Abstract] Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The method uses conversion into a symbolic representation with a self-organizing map, and grammatical inference with recurrent neural networks. We apply the method to the prediction of daily foreign exchange rates, addressing difficulties with non-stationarity, overfitting, and unequal a priori class probabilities, and we find significant predictability in comprehensive experiments covering 5 different foreign exchange rates. The method correctly predicts the direction of change for the next day with an error rate of 47.1%. The error rate reduces to around 40% when rejecting examples where the system has low confidence in its prediction. The symbolic representation aids the extraction of symbolic knowledge from the recurrent neural networks in the form of deterministic finite state automata. These automata explain the operation of the system and are often relatively simple. Rules related to well known behavior such as trend following and mean reversal are extracted. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #447250. 	[Title] Introduction to Radial Basis Function Networks [Abstract] This document is an introduction to radial basis function (RBF) networks, a type of artificial neural network for application to problems of supervised learning (e.g. regression, classification and time series prediction). It is available in either PostScript or hyper-text 2 . Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132434. 	[Title] Segmenting Time Series using Gated Experts with Simulated Annealing [Abstract] Many real-world time series are multi-stationary, where the underlying data generating process (DGP) switches between different stationary subprocesses, or modes of operation. An important problem in modeling such systems is to discover the underlying switching process, which entails identifying the number of subprocesses and the dynamics of each subprocess. For many time series, this problem is ill-defined, since there are often no obvious means to distinguish the different subprocesses. We discuss the use of nonlinear gated experts to perform the segmentation and system identification of the time series. Unlike standard gated experts methods, however, we use concepts from statistical physics to enhance the segmentation for high-noise problems where only a few experts are required. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144408. 	[Title] Neural net architectures for temporal sequence processing [Abstract] I present a general taxonomy of neural net architectures for processing time-varying patterns. This taxonomy subsumes many existing architectures in the literature, and points to several promising architectures that have yet to be examined. Any architecture that processes time-varying patterns requires two conceptually distinct components: a short-term memory that holds on to relevant past events and an associator that uses the short-term memory to classify or predict. My taxonomy is based on a characterization of short-term memory models along the dimensions of form, content, and adaptability. Experiments on predicting future values of a financial time series (US dollar-Swiss franc exchange rates) are presented using several alternative memory models. The results of these experiments serve as a baseline against which more sophisticated architectures can be compared. Neural networks have proven to be a promising alternative to traditional techniques for nonlinear temporal prediction tasks (e.g., Curtiss, Brandemuehl, & Kreider, 1992; Lapedes & Farber, 1987; Weigend, Huberman, & Rumelhart, 1992). However, temporal prediction is a particularly challenging problem because conventional neural net architectures and algorithms are not well suited for patterns that vary over time. The prototypical use of neural nets is in structural pattern recognition. In such a task, a collection of features|visual, semantic, or otherwise|is presented to a network and the network must categorize the input feature pattern as belonging to one or more classes. For example, a network might be trained to classify animal species based on a set of attributes describing living creatures such as "has tail", "lives in water", or "is carnivorous"; or a network could be trained to recognize visual patterns over a two-dimensional pixel array as a letter in fA; B; . . . ; Zg. In such tasks, the network is presented with all relevant information simultaneously. In contrast, temporal pattern recognition involves processing of patterns that evolve over time. The appropriate response at a particular point in time depends not only on the current input, but potentially all previous inputs. This is illustrated in Figure 1, which shows the basic framework for a temporal prediction problem. I assume that time is quantized into discrete steps, a sensible assumption because many time series of interest are intrinsically discrete, and continuous series can be sampled at a fixed interval. The input at time t is denoted x(t). For univariate series, this input Please summarise all the given information.

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 #1117348. 	[Title] MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS [Abstract] This paper presents a method for analyzing coupled time series using Markov models in a domain where the state space is immense. To make the parameter estimation tractable, the large state space is represented as the Cartesian product of smaller state spaces, a paradigm known as factorial Markov models. The transition matrix for this model is represented as a mixture of the transition matrices of the underlying dynamical processes. This formulation is know as mixed memory Markov models. Using this framework, we analyze the daily exchange rates for five currencies - British pound, Canadian dollar, Deutsch mark, Japanese yen, and Swiss franc as measured against the U.S. dollar. 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 #1132459. 	[Title] First experiments using a mixture of nonlinear experts for time series prediction [Abstract] This paper investigates the advantages and disadvantages of the mixture of experts (ME) model (introduced to the connectionist community in [JJNH91] and applied to time series analysis in [WM95]) on two time series where the dynamics is well understood. The first series is a computer-generated series, consisting of a mixture between a noise-free process (the quadratic map) and a noisy process (a composition of a noisy linear autoregressive and a hyperbolic tangent). There are three main results: (1) the ME model produces significantly better results than single networks; (2) it discovers the regimes correctly and also allows us to characterize the sub-processes through their variances. (3) due to the correct matching of the noise level of the model to that of the data it avoids overfitting. The second series is the laser series used in the Santa Fe competition; the ME model also obtains excellent out-of-sample predictions, allows for analysis and shows no overfitting. 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 #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 #672064. 	[Title] Direct Multi-Step Time Series Prediction Using TD() [Abstract] This paper explores the application of Temporal Difference (TD) learning (Sutton, 1988) to forecasting the behavior of dynamical systems with real-valued outputs (as opposed to game-like situations). The performance of TD learning in comparison to standard supervised learning depends on the amount of noise present in the data. In this paper, we use a deterministic chaotic time series from a low-noise laser. For the task of direct five-step ahead predictions, our experiments show that standard supervised learning is better than TD learning. The TD algorithm can be viewed as linking adjacent predictions. A similar effect can be obtained by sharing the internal representation in the network. We thus compare two architectures for both paradigms: the first architecture (separate hidden units) consists of individual networks for each of the five direct multi-step prediction tasks, the second (shared hidden units) has a single (larger) hidden layer that finds a representation from which all five predictions for the next five steps are generated. For this data set we do not find any significant difference between the two architectures. fl http://www.cs.colorado.edu/~andreas/Home.html. This paper is available as ftp://ftp.cs.colorado.edu/pub/Time-Series/MyPapers/kazlas.weigend nips7.ps.Z Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #240321. 	[Title] Nonlinear gated experts for time series: discovering regimes and avoiding overfitting [Abstract] In: International Journal of Neural Systems 6 (1995) p. 373 - 399. URL of this paper: ftp://ftp.cs.colorado.edu/pub/Time-Series/MyPapers/experts.ps.Z, or http://www.cs.colorado.edu/~andreas/Time-Series/MyPapers/experts.ps.Z University of Colorado Computer Science Technical Report CU-CS-798-95. In the analysis and prediction of real-world systems, two of the key problems are nonstationarity(often in the form of switching between regimes), and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e., on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes, (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state), and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are (1) the gating network correctly discovers the different regimes of the process, (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes), and (3) there is less overfitting compared to single networks (homogeneous multi-layer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #428610. 	[Title] Non-linear Models for Time Series Using Mixtures of Experts [Abstract] We consider a novel non-linear model for time series analysis. The study of this model emphasizes both theoretical aspects as well as practical applicability. The architecture of the model is demonstrated to be sufficiently rich, in the sense of approximating unknown functional forms, yet it retains some of the simple and intuitive characteristics of linear models. A comparison to some more established non-linear models will be emphasized, and theoretical issues are backed by prediction results for benchmark time series, as well as computer generated data sets. Efficient estimation algorithms are seen to be applicable, made possible by the mixture based structure of the model. Large sample properties of the estimators are discussed as well, in both well specified as well as misspecified settings. We also demonstrate how inference pertaining to the data structure may be made from the parameterization of the model, resulting in a better, more intuitive, understanding of the structure and performance of the model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #675649. 	[Title] Modeling Volatility using State Space Models [Abstract] In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude due to their ignoring the distinction between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40131. 	[Title] A comparison of neural net and conventional techniques for lighting control [Abstract] We compare two techniques for lighting control in an actual room equipped with seven banks of lights and photoresistors to detect the lighting level at four sensing points. Each bank of lights can be independently set to one of sixteen intensity levels. The task is to determine the device intensity levels that achieve a particular configuration of sensor readings. One technique we explored uses a neural network to approximate the mapping between sensor readings and device intensity levels. The other technique we examined uses a conventional feedback control loop. The neural network approach appears superior both in that it does not require experimentation on the fly (and hence fluctuating light intensity levels during settling, and lengthy settling times) and in that it can deal with complex interactions that conventional control techniques do not handle well. This comparison was performed as part of the "Adaptive House" project, which is described briefly. Further directions for control in the 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 #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 #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 #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 #25702. 	[Title] Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines [Abstract] A novel method for regression has been recently proposed by V. Vapnik et al. [8, 9]. The technique, called Support Vector Machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation. We implemented the SVM and tested it on the same data base of chaotic time series that was used in [1] to compare the performances of different approximation techniques, including polynomial and rational approximation, local polynomial techniques, Radial Basis Functions, and Neural Networks. The SVM performs better than the approaches presented in [1]. We also study, for a particular time series, the variability in performance with respect to the few free parameters of SVM. 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.

Paper #1154123 has the title `nan` and the 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.
`.
Question: Which category should Paper #1154123 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1154123 has the title `nan` and the 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.
`.
Question: Which category should Paper #1154123 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