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45,695
Learning polynomials with queries: The highly noisy case
, 1995
"... Given a function f mapping nvariate inputs from a finite Kearns et. al. [21] (see also [27, 28, 22]). In the setting of agfieldFintoF, we consider the task of reconstructing a list nostic learning, the learner is to make no assumptions regarding of allnvariate degreedpolynomials which agree withf ..."
Abstract

Cited by 97 (17 self)
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. In this case the providing an explanation which fits only part of it is better than nothing. Interestingly, Kearns et. al. did not consider the use of running time of our algorithm is bounded by a polynomial queries (but rather examples drawn from an arbitrary distribuand exponential ind. Our algorithm
Superexponential blind adaptive beamforming
 IEEE Trans. on Signal Processing
, 2004
"... Abstract—The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or simultaneously provide the localization of the associated sources. In thi ..."
Abstract

Cited by 7 (1 self)
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Abstract—The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or simultaneously provide the localization of the associated sources
Optimization of nonrecursive queries
 In VLDB
, 1986
"... Stateoftheart optimization approaches for relational database systems, e.g., those used in systems such as OBE, SQL/DS, and commercial INGRES. when used for queries in nontraditional database applications, suffer from two problems. First, the time complexity of their optimization algorithms, bei ..."
Abstract

Cited by 136 (5 self)
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, being combinatoric, is exponential in the number of relations to be joined in the query. Their cost is therefore prohibitive in situations such as deductive databases and logic oriented languages for knowledge bases, where hundreds of joins may be required. The second problem with the traditional
GBsplines of arbitrary order’
, 1998
"... Explicit formulae and recurrence relations for the calculation of generalized Bsplines (GBsplines) of arbitrary order are given. We derive main properties of GBsplines and their series, i.e. partition of unity, shapepreserving properties, invariance with respect to affine transformations, etc. I ..."
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Explicit formulae and recurrence relations for the calculation of generalized Bsplines (GBsplines) of arbitrary order are given. We derive main properties of GBsplines and their series, i.e. partition of unity, shapepreserving properties, invariance with respect to affine transformations, etc
Random Walk on an Arbitrary Set
"... Let I be a countably infinite set of points in R, and suppose that I has no points of accumulation and that its convex hull is the whole of R. It will be convenient to index I as {ui: i ∈ Z}, with ui < ui+1 for every i. Consider a continuoustime Markov chain Y = {Y (t) : t ≥ 0} on I, with the pr ..."
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Let I be a countably infinite set of points in R, and suppose that I has no points of accumulation and that its convex hull is the whole of R. It will be convenient to index I as {ui: i ∈ Z}, with ui < ui+1 for every i. Consider a continuoustime Markov chain Y = {Y (t) : t ≥ 0} on I, with the properties that: Y is driftless; and Y jumps only between nearest neighbours. We propose this as the obvious analogue on an irregular support set of the simple symmetric random walk on the integers; it appears natural to change from a discretetime to a continuoustime model to accommodate the irregularity, so it is not strictly speaking a generalisation. Suitably rescaled in time and space, the simple symmetric random walk converges in law to Brownian motion. In this paper we explore convergence properties for the irregular analogue. In terms of elements of the Qmatrix of Y, the requirement of freedom from drift may be written in a general form as∑ j 6=i qi,j (uj − ui) = 0 for every i. (1.1) Write `i and ri for the gaps to the left and to the right of ui: `i: = ui − ui−1, ri: = ui+1 − ui. Then the limitation to nearestneighbour jumps, namely qi,j = 0 for j 6 = i − 1, i, i+ 1, 1 allows the immediately offdiagonal elements of Q to be written as qi,i+1 = qi `i `i + ri, qi,i−1 = qi ri `i + ri where qi = −qi,i is the total jumprate out of state i which we shall not yet fix. Thus even in the regularly spaced case, the change from a discretetime to a continuoustime model permits some additional generality. Let Ti be the closed interval extending from ui halfway to each of its neighbours ui−1, ui+1, and denote by κi the length of this interval, namely κi: = 12(`i + ri). Define Di as the ‘variance ’ or ‘diffusion ’ coefficient of Y at ui:
eor Gravity at Arbitrary Pé
, 1999
"... of solid electroph van der W tion (ele acting in orders o sive and regime o particle there is n amounts tric field allow pa weak Br nificant aggregat ..."
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of solid electroph van der W tion (ele acting in orders o sive and regime o particle there is n amounts tric field allow pa weak Br nificant aggregat
Exponential Time Hypothesis
"... The quest for fast exact exponentialtime algorithms and fast parameterized algorithms for NPhard problems has been an exciting and fruitful area of research over the last decade. There is an accompanying theory of hardness based on the Exponential Time Hypothesis (ETH) and the Strong Exponential ..."
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The quest for fast exact exponentialtime algorithms and fast parameterized algorithms for NPhard problems has been an exciting and fruitful area of research over the last decade. There is an accompanying theory of hardness based on the Exponential Time Hypothesis (ETH) and the Strong Exponential
with nonexponential machines
"... Abstract. In this paper, lean buffering (i.e., the smallest level of buffering necessary and sufficient to ensure the desired production rate of a manufacturing system) is analyzed for the case of serial lines with machines having Weibull, gamma, and lognormal distributions of up and downtime. Th ..."
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. The results obtained show that: (1) the lean level of buffering is not very sensitive to the type of up and downtime distributions and dependsmainly on their coefficients of variation,CVup andCVdown; (2) the lean level of buffering is more sensitive to CVdown than to CVup but the difference in sensitivities
Results 1  10
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45,695