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139
Induction of Decision Trees
 Mach. Learn
, 1986
"... systems Abstract. The technology for building knowledgebased systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describ ..."
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Cited by 3335 (4 self)
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systems Abstract. The technology for building knowledgebased systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions. 1.
Stochastic Perturbation Theory
, 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
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Cited by 617 (31 self)
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. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variation in the perturbed quantity. Up to the higherorder terms that are ignored in the expansion, these statistics tend to be more realistic than perturbation bounds obtained in terms of norms. The technique is applied to a number of problems in matrix perturbation theory, including least squares and the eigenvalue problem. Key words. perturbation theory, random matrix, linear system, least squares, eigenvalue, eigenvector, invariant subspace, singular value AMS(MOS) subject classifications. 15A06, 15A12, 15A18, 15A52, 15A60 1. Introduction. Let A be a matrix and let F be a matrix valued function of A. Two principal problems of matrix perturbation theory are the following. Given a matrix E, pr...
Person identification using multiple cues
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... AbstractThis paper presents a person identification system based on acoustic and visual features. The system is organized as a set of nonhomogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on vis ..."
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Cited by 171 (1 self)
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AbstractThis paper presents a person identification system based on acoustic and visual features. The system is organized as a set of nonhomogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on visual ones provide data for an integration module whose performance is evaluated. A novel technique for the integration of multiple classifiers at an hybrid ranWmeasurement level is introduced using HyperBF networks. Two different methods for the rejection of an unknown person are introduced. The performance of the integrated system is shown to be superior to that of the acoustic and visual subsystems. The resulting identification system can be used to log personal access and, with minor modifications, as an identity verification system. Index TennsTemplate matching, robust statistics, correlation, face recognition, speaker recognition, learning, classification. I.
Bootstrapping with Noise: An Effective Regularization Technique
 Connection Science
, 1996
"... Bootstrap samples with noise are shown to be an effective smoothness and capacity control technique for training feedforward networks and for other statistical methods such as generalized additive models. It is shown that noisy bootstrap performs best in conjunction with weight decay regularization ..."
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Cited by 60 (16 self)
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Bootstrap samples with noise are shown to be an effective smoothness and capacity control technique for training feedforward networks and for other statistical methods such as generalized additive models. It is shown that noisy bootstrap performs best in conjunction with weight decay regularization and ensemble averaging. The twospiral problem, a highly nonlinear noisefree data, is used to demonstrate these findings. The combination of noisy bootstrap and ensemble averaging is also shown useful for generalized additive modeling, and is also demonstrated on the well known Cleveland Heart Data [7]. Keywords: Noise Injection, Combining Estimators, Pattern Classification, Two Spiral Problem Clinical Data Analysis. 1 Introduction The bootstrap technique has become one of the major tools for producing empirical confidence intervals of estimated parameters or predictors [8]. One way to view bootstrap is as a method to simulate noise inherent in the data, and thus, increase effectively t...
InformationTheoretic Analysis of Neural Coding
 J. Comp. Neuroscience
, 1998
"... We describe an approach to analyzing single and multiunit (ensemble) discharge patterns based on informationtheoretic distance measures and on empirical theories derived from work in universal signal processing. In this approach, we quantify the difference between response patterns, be they tim ..."
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Cited by 57 (13 self)
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We describe an approach to analyzing single and multiunit (ensemble) discharge patterns based on informationtheoretic distance measures and on empirical theories derived from work in universal signal processing. In this approach, we quantify the difference between response patterns, be they timevarying or not, using informationtheoretic distance measures. We apply these techniques to single and multiple unit processing of sound amplitude and sound location. These examples illustrate that neurons can simultaneously represent at least two kinds of information with different levels of fidelity. The fidelity can persist through a transient and a subsequent steadystate response, indicating that it is possible for an evolving neural code to represent information with constant fidelity. 1 Johnson et al. Analysis of Neural Coding 1 Introduction Neural coding has been classified into two broadly defined types: rate codes the average rate of spike discharge and timing codes the t...
Statistical Model Checking of BlackBox Probabilistic Systems
 In 16th conference on Computer Aided Verification (CAV’04), volume 3114 of LNCS
, 2004
"... We propose a new statistical approach to analyzing stochastic systems against specifications given in a sublogic of continuous stochastic logic (CSL). Unlike past numerical and statistical analysis methods, we assume that the system under investigation is an unknown, deployed blackbox that can be p ..."
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Cited by 41 (7 self)
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We propose a new statistical approach to analyzing stochastic systems against specifications given in a sublogic of continuous stochastic logic (CSL). Unlike past numerical and statistical analysis methods, we assume that the system under investigation is an unknown, deployed blackbox that can be passively observed to obtain sample traces, but cannot be controlled. Given a set of executions (obtained by Monte Carlo simulation) and a property, our algorithm checks, based on statistical hypothesis testing, whether the sample provides evidence to conclude the satisfaction or violation of a property, and computes a quantitative measure (pvalue of the tests) of confidence in its answer; if the sample does not provide statistical evidence to conclude the satisfaction or violation of the property, the algorithm may respond with a "don't know" answer. We implemented our algorithm in a Javabased prototype tool called VeStA, and experimented with the tool using case studies analyzed in [15]. Our empirical results show that our approach may, at least in some cases, be faster than previous analysis methods.
Monte Carlo and QuasiMonte Carlo methods
 Acta Numerica
, 1998
"... Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N ~ 1 ^ 2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including conve ..."
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Cited by 35 (1 self)
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Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N ~ 1 ^ 2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasirandom (also called lowdiscrepancy) sequences, which are a deterministic alternative to random or pseudorandom sequences. The points in a quasirandom sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasiMonte Carlo, has a convergence rate of approximately O((log N^N ' 1). For quasiMonte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less
Dapple: Improved techniques for finding spots on DNA microarrays
, 2000
"... A key step in experiments using DNA microarrays is locating the thousands of individual spots in a scanned array image. Each spot provides quantitative information about a distinct DNA sequence, so it is imperative that spots be found and quantitated accurately. Spot finding is complicated by variat ..."
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Cited by 30 (0 self)
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A key step in experiments using DNA microarrays is locating the thousands of individual spots in a scanned array image. Each spot provides quantitative information about a distinct DNA sequence, so it is imperative that spots be found and quantitated accurately. Spot finding is complicated by variations in the positions and sizes of spots and by the presence of artifacts and background noise in microarray images. We describe Dapple, a new spot finding implementation for microarrays on glass slides. Dapple finds spots using morphological information which is robust to both variation and artifacts. It achieves high spot finding throughput and accuracy by learning to evaluate the quality of candidate spots from examples supplied by the user. Dapple’s techniques are useful for improving the accuracy of data acquisition from DNA microarrays. Availability: Dapple runs on UNIXlike systems using the Qt GUI toolkit for X. The C++ source code and a prebuilt Linux binary are available at
On statistical model checking of stochastic systems
 In Etessami, K., Rajamani, S.K., eds.: CAV. Volume 3576 of Lecture Notes in Computer Science
, 2005
"... Abstract. Statistical methods to model check stochastic systems have been, thus far, developed only for a sublogic of continuous stochastic logic (CSL) that does not have steady state operator and unbounded until formulas. In this paper, we present a statistical model checking algorithm that also ve ..."
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Cited by 28 (2 self)
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Abstract. Statistical methods to model check stochastic systems have been, thus far, developed only for a sublogic of continuous stochastic logic (CSL) that does not have steady state operator and unbounded until formulas. In this paper, we present a statistical model checking algorithm that also verifies CSL formulas with unbounded untils. The algorithm is based on Monte Carlo simulation of the model and hypothesis testing of the samples, as opposed to sequential hypothesis testing. We have implemented the algorithm in a tool called VESTA, and found it to be effective in verifying several examples. 1