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CrossComposition: A New Technique for Kernelization Lower Bounds
, 2011
"... We introduce a new technique for proving kernelization lower bounds, called crosscomposition. A classical problem L crosscomposes into a parameterized problem Q if an instance of Q with polynomially bounded parameter value can express the logical OR of a sequence of instances of L. Building on wor ..."
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Cited by 40 (7 self)
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We introduce a new technique for proving kernelization lower bounds, called crosscomposition. A classical problem L crosscomposes into a parameterized problem Q if an instance of Q with polynomially bounded parameter value can express the logical OR of a sequence of instances of L. Building
CrossComposition: A New Technique for
"... We introduce a new technique for proving kernelization lower bounds, called crosscomposition. A classical problem L crosscomposes into a parameterized problem Q if an instance of Q with polynomially bounded parameter value can express the logical OR of a sequence of instances of L. Building on wor ..."
Abstract
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We introduce a new technique for proving kernelization lower bounds, called crosscomposition. A classical problem L crosscomposes into a parameterized problem Q if an instance of Q with polynomially bounded parameter value can express the logical OR of a sequence of instances of L. Building
The xKernel: An Architecture for Implementing Network Protocols
 IEEE Transactions on Software Engineering
, 1991
"... This paper describes a new operating system kernel, called the xkernel, that provides an explicit architecture for constructing and composing network protocols. Our experience implementing and evaluating several protocols in the xkernel shows that this architecture is both general enough to acc ..."
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Cited by 663 (21 self)
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This paper describes a new operating system kernel, called the xkernel, that provides an explicit architecture for constructing and composing network protocols. Our experience implementing and evaluating several protocols in the xkernel shows that this architecture is both general enough
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 780 (22 self)
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Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information
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
Scheduler Activations: Effective Kernel Support for the UserLevel Management of Parallelism
 ACM Transactions on Computer Systems
, 1992
"... Threads are the vehicle,for concurrency in many approaches to parallel programming. Threads separate the notion of a sequential execution stream from the other aspects of traditional UNIXlike processes, such as address spaces and I/O descriptors. The objective of this separation is to make the expr ..."
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Cited by 475 (21 self)
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the expression and control of parallelism sufficiently cheap that the programmer or compiler can exploit even finegrained parallelism with acceptable overhead. Threads can be supported either by the operating system kernel or by userlevel library code in the application address space, but neither approach has
On µkernel construction
 Symposium on Operating System Principles
, 1995
"... From a softwaretechnology point of view, thekernel concept is superior to large integrated kernels. On the other hand, it is widely believed that (a)kernel based systems are inherently inefficient and (b) they are not sufficiently flexible. Contradictory to this belief, we show and support by doc ..."
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Cited by 424 (25 self)
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From a softwaretechnology point of view, thekernel concept is superior to large integrated kernels. On the other hand, it is widely believed that (a)kernel based systems are inherently inefficient and (b) they are not sufficiently flexible. Contradictory to this belief, we show and support
A Scheme for RealTime Channel Establishment in WideArea Networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 1990
"... Multimedia communication involving digital audio and/or digital video has rather strict delay requirements. A realtime channel is defined in this paper as a simplex connection between a source and a destination characterized by parameters representing the performance requirements of the client. A r ..."
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Cited by 710 (31 self)
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realtime service is capable of creating realtime channels on demand and guaranteeing their performance. These guarantees often take the form of lower bounds on the bandwidth allocated to a channel and upper bounds on the delays to be experienced by a packet on the channel. In this paper
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Results 1  10
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435,528