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Results 11 - 20
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1,077
Refinement of recursive procedures with parameters in PVS
, 2004
"... We present a shallow embedding in PVS of a predicate transformer semantics of an imperative language suitable for reasoning about recursive procedures with parameters and local variables. We use the PVS dependent type mechanism for implementing program variables of different types. We use an uninter ..."
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We present a shallow embedding in PVS of a predicate transformer semantics of an imperative language suitable for reasoning about recursive procedures with parameters and local variables. We use the PVS dependent type mechanism for implementing program variables of different types. We use
Geodesic Mean Shift
, 2004
"... In this paper we introduce a versatile and robust method for analyzing the feature space associated with a given surface. The method is based on the mean-shift operator which was shown to be successful in image and video processing. Its strength stems from the fact that it works in a single space of ..."
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Cited by 7 (1 self)
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In this paper we introduce a versatile and robust method for analyzing the feature space associated with a given surface. The method is based on the mean-shift operator which was shown to be successful in image and video processing. Its strength stems from the fact that it works in a single space
Covariate Shift by Kernel Mean Matching
"... Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve this goal by matching covariate distributions between training and test sets in a high dimensional feature ..."
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Cited by 29 (1 self)
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mean and the test feature mean, a transductive bound on the expected loss of an algorithm trained on the reweighted data, and a connection to single class SVMs. While our method is designed to deal with the case of simple covariate shift (in the sense of Chapter??), we have also found benefits
Mean Shift Clustering
, 2005
"... Mean shift represents a general non-parametric mode finding/clustering procedure. In contrast to the classic K-means clustering approach (Duda, Hart & Stork, 2001), there are no embedded assumptions on the shape of the distribution nor the number of modes/clusters. Mean shift was first proposed ..."
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Mean shift represents a general non-parametric mode finding/clustering procedure. In contrast to the classic K-means clustering approach (Duda, Hart & Stork, 2001), there are no embedded assumptions on the shape of the distribution nor the number of modes/clusters. Mean shift was first proposed
Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis
, 2004
"... We consider using out-of-sample mean squared prediction errors (MSPEs) to evaluate the null that a given series follows a zero mean martingale difference against the alternative that it is linearly predictable. Under the null of no predictability, the population MSPE of the null “no change” model eq ..."
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Cited by 119 (14 self)
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normal test that properly accounts for the upward shift of the sample MSPE of the alternative model. Our simulations indicate that our proposed procedure works well.
The Advent of Recursion . . .
"... The term ‘recursive’ has had different meanings during the past two centuries among various communities of scholars. Its historical epistemology has already been described by Soare (1996) with respect to the mathematicians, logicians, and recursive-function theorists. The computer practitioners, on ..."
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, on the other hand, are discussed in this paper by focusing on the definition and implementation of the ALGOL60 programming language. Recursion entered ALGOL60 in two novel ways: (i) syntactically with what we now call BNF notation, and (ii) dynamically by means of the recursive procedure. As is shown, both (i
Fast nonparametric clustering with Gaussian blurring mean-shift
- In Int. Conf. on Machine Learning
, 2006
"... We revisit Gaussian blurring mean-shift (GBMS), a procedure that iteratively sharpens a dataset by moving each data point according to the Gaussian mean-shift algorithm (GMS). (1) We give a criterion to stop the procedure as soon as clustering structure has arisen and show that this reliably produce ..."
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Cited by 36 (2 self)
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We revisit Gaussian blurring mean-shift (GBMS), a procedure that iteratively sharpens a dataset by moving each data point according to the Gaussian mean-shift algorithm (GMS). (1) We give a criterion to stop the procedure as soon as clustering structure has arisen and show that this reliably
Predicate Transformers for Recursive Procedures With Local Variables
, 1999
"... The weakest precondition semantics of recursive procedures with local variables are developed for an imperative language with demonic and angelic operators for unbounded nondeterminate choice. This does not require stacking of local variables. The formalism serves as a foundation for a proof rule fo ..."
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Cited by 4 (1 self)
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, this is also possible for recursive procedures by means of methods analogous to those developed be...
Synergism in low level vision
- In
, 2002
"... Guiding image segmentation with edge information is an often employed strategy in low level computer vision. To improve the trade-off between the sensitivity of homo-geneous region delineation and the oversegmentation of the image, we have incorporated a recently proposed edge magnitude/confidence m ..."
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Cited by 141 (4 self)
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/confidence map into a color image segmenter based on the mean shift procedure. The new method can recover regions with weak but sharp boundaries and thus can provide a more accurate input for high level interpre-tation modules. The Edge Detection and Image Segmen-tatiON (EDISON) system, available for download
Recursive Optimization Procedure for Fuzzy-Logic Controller Synthesis
- PROC. AMERICAN CONTROL CONF
, 1998
"... We present a new method for the synthesis of fuzzylogic controllers (flcs) for amplitude-sensitive nonlinear plants based on sinusoidal-input describing-function (sidf) methods plus step-response optimization. This method involves a two-step process wherein an initial controller is obtained via the ..."
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Cited by 4 (2 self)
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specified set of input amplitudes. By "recursive" we mean that the average step-response obtained from step k is used as the objective for the k+1 optimization problem, and the process iterates until convergence, with the objective of achieving closed-loop system performance that is as insensitive
Results 11 - 20
of
1,077
