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Average Error Filter Wavelength
, 21
"... unication, and FP = further processing. ffl Fig. 4 a), b) Stereo pair. c) Disparity map, the filter's width, oe = =2:1, is 6 pixels. ffl Fig. 5 a) Image of a chair and a dustbin. b) Disparity map, oe = 6 pixels. c) The filter's energy ae in the nonsingular regions that survive constraint ..."
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constraint (9). ffl Fig. 6 One of the images of the Translating Tree sequence. ffl Fig. 7 The disparity field of the Translating Tree computed for an input image of size 128 \Theta 128 (averaged). ffl Fig. 8 Behavior of the algorithm's error as a function of the filter wavelength, = 2:1 oe
Average Error for Spectral Asymptotics on Surfaces
"... Let N(t) denote the eigenvalue counting function of the Laplacian on a compact surface of constant nonnegative curvature, with or without boundary. We define a refined asymptotic formula Ñ(t) = At+Bt1/2 +C, where the constants are expressed in terms of the geometry of the surface and its boundary ..."
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boundary, and consider the average error A(t) = 1t ∫ t 0 D(s) ds for D(t) = N(t) − Ñ(t). We present a conjecture for the asymptotic behavior of A(t), and study some examples that support the conjecture. “The mills of God grind slowly, yet they grind exceeding small.” Proverb 1
Reducing the Average Error of Underresolved Approximations
"... There exist problems of practical interest (in particular in turbulence) whose solutions are too complex to be accurately resolved numerically, but where one is interested only in large scale features averaged over the random details. We define the appropriate averages as expectations conditioned by ..."
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There exist problems of practical interest (in particular in turbulence) whose solutions are too complex to be accurately resolved numerically, but where one is interested only in large scale features averaged over the random details. We define the appropriate averages as expectations conditioned
Approximation for Average Error Probability of BPSK in the Presence of Phase Error
"... Abstract—Phase error in communications systems degrades error performance. In this paper, we present a simple approximation for the average error probability of the binary phase shift keying (BPSK) in the presence of phase error having a uniform distribution on arbitrary intervals. For the simple ap ..."
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Abstract—Phase error in communications systems degrades error performance. In this paper, we present a simple approximation for the average error probability of the binary phase shift keying (BPSK) in the presence of phase error having a uniform distribution on arbitrary intervals. For the simple
Validation of Average Error Rate Over Classifiers
, 1997
"... We examine methods to estimate the average and variance of test error rates over a set of classifiers. We begin with the process of drawing a classifier at random for each example. Given validation data, the average test error rate can be estimated as if validating a single classifier. Given the tes ..."
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Cited by 3 (3 self)
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We examine methods to estimate the average and variance of test error rates over a set of classifiers. We begin with the process of drawing a classifier at random for each example. Given validation data, the average test error rate can be estimated as if validating a single classifier. Given
Ensemble Methods in Machine Learning
 MULTIPLE CLASSIFIER SYSTEMS, LBCS1857
, 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging, and boostin ..."
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Cited by 625 (3 self)
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Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging
Basecalling of automated sequencer traces using phred. I. Accuracy Assessment
 GENOME RES
, 1998
"... The availability of massive amounts of DNA sequence information has begun to revolutionize the practice of biology. As a result, current largescale sequencing output, while impressive, is not adequate to keep pace with growing demand and, in particular, is far short of what will be required to obta ..."
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Cited by 1653 (4 self)
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accuracy. phred appears to be the first basecalling program to achieve a lower error rate than the ABI software, averaging 40%–50 % fewer errors in the data sets examined independent of position in read, machine running conditions, or sequencing chemistry.
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.
 Machine Learning,
, 1999
"... Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several vari ..."
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Cited by 707 (2 self)
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in the average tree size in AdaBoost trials and its success in reducing the error. We compare the meansquared error of voting methods to nonvoting methods and show that the voting methods lead to large and significant reductions in the meansquared errors. Practical problems that arise in implementing boosting
Results 1  10
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14,044