Results 1  10
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14
Wrappers For Performance Enhancement And Oblivious Decision Graphs
, 1995
"... In this doctoral dissertation, we study three basic problems in machine learning and two new hypothesis spaces with corresponding learning algorithms. The problems we investigate are: accuracy estimation, feature subset selection, and parameter tuning. The latter two problems are related and are stu ..."
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Cited by 107 (8 self)
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In this doctoral dissertation, we study three basic problems in machine learning and two new hypothesis spaces with corresponding learning algorithms. The problems we investigate are: accuracy estimation, feature subset selection, and parameter tuning. The latter two problems are related and are studied under the wrapper approach. The hypothesis spaces we investigate are: decision tables with a default majority rule (DTMs) and oblivious readonce decision graphs (OODGs).
Statistical modeling: The two cultures
 Statistical Science
, 2001
"... Abstract. There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated bya given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical communityhas been committed to the ..."
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Cited by 93 (0 self)
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Abstract. There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated bya given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical communityhas been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theoryand practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools. 1.
Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems
 IEEE Transactions on Neural Networks
, 1997
"... In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole ..."
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Cited by 66 (2 self)
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In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole problem as a state space search, we first describe the general issues in constructive algorithms, with special emphasis on the search strategy. A taxonomy, based on the differences in the state transition mapping, the training algorithm and the network architecture, is then presented. Keywords Constructive algorithm, structure learning, state space search, dynamic node creation, projection pursuit regression, cascadecorrelation, resourceallocating network, group method of data handling. I. Introduction A. Problems with Fixed Size Networks I N recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. Among...
The variable selection problem
 Journal of the American Statistical Association
, 2000
"... The problem of variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables ..."
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Cited by 39 (2 self)
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The problem of variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors, but there is uncertainty about which subset to use. This vignette reviews some of the key developments which have led to the wide variety of approaches for this problem. 1
On the cost of data analysis
 Journal of Computational and Graphical Statistics
, 1992
"... A regression analysis usually consists of several stages such as variable selection, transformation and residual diagnosis. Inference is often made from the selected model without regard to the model selection methods that preceeded it. This can result in overoptimistic and biased inferences. We fir ..."
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Cited by 18 (2 self)
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A regression analysis usually consists of several stages such as variable selection, transformation and residual diagnosis. Inference is often made from the selected model without regard to the model selection methods that preceeded it. This can result in overoptimistic and biased inferences. We first characterize data analytic actions as functions acting on regression models. We investigate the extent of the problem and test bootstrap, jackknife and sample splitting methods for ameliorating it. We also demonstrate an interactive LISPSTAT system for assessing the cost of the data analysis while it is taking place.
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...
Static Neural Network Process Models: Considerations And Case Studies
, 1998
"... Neural networks are beginning to be used for the modeling of complex manufacturing processes, usually for process and quality control. Often these models are used to identify optimal process settings. Since a neural network is an empirical model, it is highly dependent on the data used in constru ..."
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Cited by 6 (3 self)
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Neural networks are beginning to be used for the modeling of complex manufacturing processes, usually for process and quality control. Often these models are used to identify optimal process settings. Since a neural network is an empirical model, it is highly dependent on the data used in construction and validation. Using data directly from production ensures availability and fidelity, however the samples may not reflect the entire range of probable operation and, in particular, may not include the optimal process settings. Supplementing production data with observations gathered from designed experiments alleviates the problem of overly focused or incomplete production data sets. This paper considers practical aspects of building and validating neural network models of manufacturing processes, and illustrates the recommended approaches with two diverse case studies.
Myneni, “Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles
 France,” J. Geophys. Res.—Atmos
"... collected in a 3 3 km agricultural (grasses and cereal crops) area near Avignon, France, and 30 m resolution Enhanced Thematic Mapper (ETM+) image. Estimates of the accuracy, precision, and uncertainty with which the ETM+ data convey information about LAI underlie the derivation of a 30 m resolution ..."
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Cited by 5 (4 self)
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collected in a 3 3 km agricultural (grasses and cereal crops) area near Avignon, France, and 30 m resolution Enhanced Thematic Mapper (ETM+) image. Estimates of the accuracy, precision, and uncertainty with which the ETM+ data convey information about LAI underlie the derivation of a 30 m resolution reference LAI map by accounting for both field measurement and satellite observation errors. The 30 m reference LAI was then extrapolated from sampling points to a 58 km 2 area without loss in the quality and was degraded to a 1 km resolution LAI map. The latter was taken as a reference to assess the quality of the MODIS LAI product. Comparison of the reference and corresponding MODIS retrievals suggests that Collection 4 MODIS LAI is accurate to within an accuracy of 0.3 with a precision and uncertainty of 0.23 and 0.38, respectively. It was found that the Collection 3 MODIS land cover product, input to the Collection 4 operational LAI algorithm, misclassified the 58 km 2 area as broadleaf crops. The use of correct biome type in the operational processing improves the accuracy in LAI by a factor of 2 with an almost unchanged precision and uncertainty. Our results also indicate that the retrieval of LAI from satellite data is an
Validation and Verification
 in Artificial Neural Networks for Civil Engineers: Fundamentals and Applications
, 1997
"... Pushbutton automation is an important milestone for verification systems and a likely requirement for mainstream acceptance of the notion of ”verified software”. Multiple, logicallyequivalent specifications may differ widely from the standpoint of their ability to contribute to verifiable client c ..."
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Cited by 2 (1 self)
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Pushbutton automation is an important milestone for verification systems and a likely requirement for mainstream acceptance of the notion of ”verified software”. Multiple, logicallyequivalent specifications may differ widely from the standpoint of their ability to contribute to verifiable client code. Using the types of problems considered at the VSTTE 2010 competition as motivation, we explore the question of specifying the same programming concept (lists) using completely different mathematical models and in each case examine the provability of client code based on that concept. The ultimate goal is to develop a set of specification patterns that aid software developers in attaining the goal of verified software. Initial results from an experimental exploration are presented along with some hypotheses for bestpractices for specification design.
Wave Solder Process Control Modeling Using A Neural Network Approach
 In Intelligent Engineering Systems Through Arti® cial Neural Networks
, 1994
"... : We discuss the formulation and results of a simple backpropagation approach to the control of wave soldering of printed circuit cards. Small lot sizes and a large number of different circuit card designs have complicated selection of the tunable process settings at the large manufacturer we w ..."
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: We discuss the formulation and results of a simple backpropagation approach to the control of wave soldering of printed circuit cards. Small lot sizes and a large number of different circuit card designs have complicated selection of the tunable process settings at the large manufacturer we worked with. Use of a neural network predictive model results in improved precision relative to the currently used multivariate linear model. INTRODUCTION The wave solder process involves (1) preheating, (2) fluxing, (3) soldering using a wave of solder, (4) cleaning, and (5) quality control. The process must be adapted according to the design (mass, size, component density, component type, etc.) of the circuit card to optimize quality, i.e. minimize solder connection defects. Process parameters which are controllable are the preheat temperatures and the line speed. Circuit card manufacturers produce products of great diversity in small lot sizes, compounding the selection of good process...