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Exact Algorithms for Set Multicover and Multiset Multicover Problems
"... Abstract. Given a universe N containing n elements and a collection of multisets or sets over N, the multiset multicover (MSMC) or the set multicover (SMC) problem is to cover all elements at least a number of times as specified in their coverage requirements with the minimum number of multisets or ..."
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Abstract. Given a universe N containing n elements and a collection of multisets or sets over N, the multiset multicover (MSMC) or the set multicover (SMC) problem is to cover all elements at least a number of times as specified in their coverage requirements with the minimum number of multisets
Partial Multicovering and the dconsecutive Ones Property
, 2011
"... A dinterval is the union of d disjoint intervals on the real line. In the dinterval stabbing problem (dis) we are given a set of dintervals and a set of points, each dinterval I has a stabbing requirement r(I) and each point has a weight, and the goal is to find a minimum weight multiset of poi ..."
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present a (ρ+d−1 ρ)approximation algorithm for prize collecting dis, where ρ = minI r(I), and an O(d)approximation algorithm for partial dis. We obtain the latter result by designing a general framework for approximating partial multicovering problems that extends the framework for approximating
On the Set MultiCover Problem in Geometric Settings
 IN PROC. 25TH ANNU. ACM SYMPOS. COMPUT. GEOM
, 2009
"... We consider the set multicover problem in geometric settings. Given a set of points P and a collection of geometric shapes (or sets) F, we wish to nd a minimum cardinality subset of F such that each point p ∈ P is covered by (contained in) at least d(p) sets. Here d(p) is an integer demand (require ..."
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Cited by 19 (4 self)
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(requirement) for p. When the demands d(p) = 1 for all p, this is the standard set cover problem. The set cover problem in geometric settings admits an approximation ratio that is better than that for the general version. In this paper, we show that similar improvements can be obtained for the multicover
A note on multicovering with disks
 Comput. Geom
"... In theDisk Multicover problem the input consists of a set P of n points in the plane, where each point p ∈ P has a covering requirement k(p), and a set B of m base stations, where each base station b ∈ B has a weight w(b). If a base station b ∈ B is assigned a radius r(b), it covers all points in th ..."
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Cited by 3 (0 self)
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Multicover problem is a closely related problem, in which the set P is a polygon (possibly with holes), and the goal is to find a minimum weight radius assignment that covers each point in P at least K times. We present a 3αkmaxapproximation algorithm for Disk Multicover, where kmax is the maximum covering
Bucket game with applications to set multicover and dynamic page migration
 in: Proc. of the 13th European Symp. on Algorithms (ESA
, 2005
"... Abstract. We present a simple twoperson Bucket Game, based on throwing balls into buckets, and we discuss possible players ’ strategies. We use these strategies to create an approximation algorithm for a generalization of the well known Set Cover problem, where we need to cover each element by at ..."
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Cited by 5 (4 self)
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Abstract. We present a simple twoperson Bucket Game, based on throwing balls into buckets, and we discuss possible players ’ strategies. We use these strategies to create an approximation algorithm for a generalization of the well known Set Cover problem, where we need to cover each element
Approximating the Online Set Multicover Problems Via Randomized Winnowing
 IN 9TH WORKSHOP ON ALGORITHMS AND DATA STRUCTURES (WADS
, 2005
"... In this paper, we consider the weighted online set kmulticover problem. In this problem, we have an universe V of elements, a family S of subsets of V with a positive real cost for every S ∈S, and a “coverage factor” (positive integer) k. A subset {i0,i1,...} ⊆ V of elements are presented online i ..."
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Cited by 1 (1 self)
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. The goal is to minimize the total cost of the selected sets 3. In this paper, we describe a new randomized algorithm for the online multicover problem based on the randomized winnowing approach of [11]. This algorithm generalizes and improves some earlier results in [1]. We also discuss lower bounds
Approximate Signal Processing
, 1997
"... It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tra ..."
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Cited by 516 (2 self)
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number of ideas and approaches to approximate processing as currently being formulated in the computer science community. We then present four examples of signal processing algorithms/systems that are structured with these goals in mind. These examples may be viewed as partial inroads toward the ultimate
Face recognition: features versus templates
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1993
"... AbstractOver the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per per ..."
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Cited by 737 (25 self)
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person). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almostgreylevel template matching. The results obtained on the testing
The pyramid match kernel: Discriminative classification with sets of image features
 IN ICCV
, 2005
"... Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernelbased classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondenc ..."
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Cited by 546 (29 self)
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Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernelbased classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve
Results 1  10
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