Results 1  10
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46
When Networks Disagree: Ensemble Methods for Hybrid Neural Networks
, 1993
"... This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, we construct a hybrid estimator which is as good or better in the MSE sense than any estimator in the population. We argu ..."
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Cited by 296 (2 self)
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This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, we construct a hybrid estimator which is as good or better in the MSE sense than any estimator in the population. We argue that the ensemble method presented has several properties: 1) It efficiently uses all the networks of a population  none of the networks need be discarded. 2) It efficiently uses all the available data for training without overfitting. 3) It inherently performs regularization by smoothing in functional space which helps to avoid overfitting. 4) It utilizes local minima to construct improved estimates whereas other neural network algorithms are hindered by local minima. 5) It is ideally suited for parallel computation. 6) It leads to a very useful and natural measure of the number of distinct estimators in a population. 7) The optimal parameters of the ensemble estimator are given in clo...
Improving Regression Estimation: Averaging Methods for Variance Reduction with Extensions to General Convex Measure Optimization
, 1993
"... ..."
Bias and Variance of Validation Methods for Function Approximation Neural Networks Under Conditions of Sparse Data
 IEEE Transactions on Systems, Man, and Cybernetics, Part C
, 1998
"... Neural networks must be constructed and validated with strong empirical dependence, which is difficult under conditions of sparse data. This paper examines the most common methods of neural network validation along with several general validation methods from the statistical resampling literature ..."
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Cited by 11 (6 self)
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Neural networks must be constructed and validated with strong empirical dependence, which is difficult under conditions of sparse data. This paper examines the most common methods of neural network validation along with several general validation methods from the statistical resampling literature as applied to function approximation networks with small sample sizes. It is shown that an increase in computation, necessary for the statistical resampling methods, produces networks that perform better than those constructed in the traditional manner. The statistical resampling methods also result in lower variance of validation, however some of the methods are biased in estimating network error. 1. INTRODUCTION To be beneficial, system models must be validated to assure the users that the model emulates the actual system in the desired manner. This is especially true of empirical models, such as neural network and statistical models, which rely primarily on observed data rather th...
On the Voronoi regions of certain lattices
 MIT Libraries. Downloaded on October 28, 2008 at 20:11 from IEEE Xplore. Restrictions apply. et al.: DESIGN OF SPHERICAL LATTICE SPACE–TIME CODES 4865
, 1984
"... convex polytope Abstract. The Voronoi region of a lattice Ln R is the convex polytope consisting of all points of I that are closer to the origin than to any other point of Ln. In this paper we calculate the second moments of the Voronoi regions of the lattices E6*, E7*, K12, A16 and A24. The result ..."
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Cited by 9 (4 self)
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convex polytope Abstract. The Voronoi region of a lattice Ln R is the convex polytope consisting of all points of I that are closer to the origin than to any other point of Ln. In this paper we calculate the second moments of the Voronoi regions of the lattices E6*, E7*, K12, A16 and A24. The results show that these lattices are
Multilabel boosting for image annotation by structural grouping sparsity
 in ACM Multimedia, 2010
"... We can obtain highdimensional heterogenous features from realworld images to describe their various aspects of visual characteristics, such as color, texture and shape etc. Different kinds of heterogenous features have different intrinsic discriminative power for image understanding. The selecti ..."
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Cited by 4 (0 self)
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We can obtain highdimensional heterogenous features from realworld images to describe their various aspects of visual characteristics, such as color, texture and shape etc. Different kinds of heterogenous features have different intrinsic discriminative power for image understanding. The selection of groups of discriminative features for certain semantics is hence crucial to make the image understanding more interpretable. This paper formulates the multilabel image annotation as a regression model with a regularized penalty. We call it Multilabel Boosting by the selection of heterogeneous features with structural Grouping Sparsity (MtBGS). MtBGS induces a (structural) sparse selection model to identify subgroups of homogenous features for predicting a certain label. Moreover, the correlations among multiple tags are utilized in MtBGS to boost the performance of multilabel annotation. Extensive experiments on public image datasets show that the proposed approach has better multilabel image annotation performance and leads to a quite interpretable model for image understanding.
Modelling Heterogeneity in Cetacean Surveys
, 2000
"... Methods for improving estimation of cetacean abundance from line transect and mark recapture surveys are proposed. Using either generalized linear or generalized additive models (GLMs or GAMs), two approaches are suggested which allow heterogeneity in the spatial distribution of cetaceans to be mode ..."
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Cited by 4 (0 self)
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Methods for improving estimation of cetacean abundance from line transect and mark recapture surveys are proposed. Using either generalized linear or generalized additive models (GLMs or GAMs), two approaches are suggested which allow heterogeneity in the spatial distribution of cetaceans to be modelled from standard line transect data. In the first approach, the transect lines are divided into smaller discrete units, and the expected number of detections in each unit is modelled using explanatory spatial covariates. In the second approach, the response is derived from the observed waiting times (or distances) between detections. Fitting this model within the usual GLM or GAM framework would require restrictive assumptions, therefore an iterative procedure is formulated which enables a realistic model to be fitted. Alternatively, it is shown how this approach can be framed as a point process model, and it is suggested how the likelihood for the observed alongtrackline distances could be maximized. The methods are illustrated using line transect data from a survey of Antarctic minke whales. A surface representing the variation in density throughout the survey region is obtained, from which abundance may be estimated by numerical integration. It is also
BioMed Central
, 2006
"... A novel approach to phylogenetic tree construction using stochastic optimization and clustering ..."
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Cited by 4 (2 self)
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A novel approach to phylogenetic tree construction using stochastic optimization and clustering
Applying a robust heteroscedastic probabilistic neural network to analog fault detection and classification
 IEEE Transactions on
, 2000
"... Abstract—The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques such as neura ..."
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Cited by 3 (0 self)
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Abstract—The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques such as neural networks have been employed to automate classification. The major drawback to such techniques has been the implicit assumption that the variances of the responses of faulty circuits have been the same as each other and the same as that of the faultfree circuit. This assumption can be shown to be false. Neural networks, moreover, have proved to be slow. This paper describes a new neural network structure that clusters responses assuming different means and variances. Sophisticated statistical techniques are employed to handle situations where the variance tends to zero, such as happens with a fault that causes a response to be stuck at a supply rail. Two example circuits are used to show that this technique is significantly more accurate than other classification methods. A set of responses can be classified in the order of 1 s. Index Terms—Automatic test pattern generation (ATPG) testing, faultdiagnosis, quiescent supply current (IDDQ), mixedsignal_test. I.
Fault detection and classification in analogue integrated circuits using robust heteroscedastic probabilistic neural networks
 4th IEEE International Mixed Signal Testing Workshop, The Hague, The
, 1998
"... A new neural network method has been used to distinguish faulty from faultfree circuit responses. This technique is significantly more accurate than other classification methods. A set of responses can be classified in the order of 1 second. 1. ..."
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Cited by 2 (0 self)
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A new neural network method has been used to distinguish faulty from faultfree circuit responses. This technique is significantly more accurate than other classification methods. A set of responses can be classified in the order of 1 second. 1.
The Variance of the Mutual Information Estimator
, 1997
"... In the case of two signals with independent pairs of observations (xn ; yn) a statistic to estimate the variance of the mutual information estimator has been derived earlier. We present such a statistic for dependent pairs. To derive this statistic it is necessary to avail of a reliable statistic t ..."
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Cited by 2 (0 self)
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In the case of two signals with independent pairs of observations (xn ; yn) a statistic to estimate the variance of the mutual information estimator has been derived earlier. We present such a statistic for dependent pairs. To derive this statistic it is necessary to avail of a reliable statistic to estimate the variance of the sample mean in case of dependent observations. We derive and discuss this statistic and a statistic to estimate the variance of the mutual information estimator. These statistics are verified by simulations. Key words: sample mean, variance, statistic, mutual information, entropy, likelihood, dependent samples. 1 Introduction To estimate timedelays between recordings of electroencephalogram (EEG) signals Mars and van Arragon introduced a method based on maximum mutual information [13]. The method was modified and extended [15, 16]. Mutual information is used to measure the dependence of nonlinearly dependent random variables and widely applied to stochasti...