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878,336
An exponential number of generalized Kerdock codes
 Inform. Control
, 1982
"... If n 1 is an odd composite integer then there are at least 2 tl/2)~f ~ pairwise inequivalent binary errorcorrecting codes of length 2 n, size 22n, and minimum distance 2 n 12(1/2)n 1. 1. ..."
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Cited by 11 (6 self)
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If n 1 is an odd composite integer then there are at least 2 tl/2)~f ~ pairwise inequivalent binary errorcorrecting codes of length 2 n, size 22n, and minimum distance 2 n 12(1/2)n 1. 1.
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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of probability distributions — are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, we develop general variational representations of the problems of computing
Construction of Composite Numbers by Recursively Exponential Numbers
, 2005
"... In this paper, we give some constructions of composite numbers N, such that every number less than or equal to M divides N. Therefore, lcm(1, 2, 3, · · · , M)  N. We call such numbers divisible up to M. ..."
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In this paper, we give some constructions of composite numbers N, such that every number less than or equal to M divides N. Therefore, lcm(1, 2, 3, · · · , M)  N. We call such numbers divisible up to M.
A CONGRUENCE FOR A CLASS OF EXPONENTIAL NUMBERS
, 1983
"... A sequence of exponential generating function as numbers, say Pn, is defined by i t s exponential ^2>Pnx n /.n \ = exp{^(x)} ..."
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A sequence of exponential generating function as numbers, say Pn, is defined by i t s exponential ^2>Pnx n /.n \ = exp{^(x)}
Large margin methods for structured and interdependent output variables
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
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Cited by 612 (12 self)
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to accomplish this, we propose to appropriately generalize the wellknown notion of a separation margin and derive a corresponding maximummargin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm
WideArea Traffic: The Failure of Poisson Modeling
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1995
"... Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session and con ..."
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Cited by 1772 (24 self)
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Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session
High dimensional graphs and variable selection with the Lasso
 ANNALS OF STATISTICS
, 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
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Cited by 751 (23 self)
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joining some distinct connectivity components of the graph, consistent estimation for sparse graphs is achieved (with exponential rates), even when the number of variables grows as the number of observations raised to an arbitrary power.
Graphbased algorithms for Boolean function manipulation
 IEEE TRANSACTIONS ON COMPUTERS
, 1986
"... In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on th ..."
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Cited by 3499 (47 self)
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on the ordering of decision variables in the graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications have a more reasonable representation. Our algorithms have time complexity proportional
Performance Analysis of the IEEE 802.11 Distributed Coordination Function
, 2000
"... Recently, the IEEE has standardized the 802.11 protocol for Wireless Local Area Networks. The primary medium access control (MAC) technique of 802.11 is called distributed coordination function (DCF). DCF is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary slott ..."
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Cited by 1831 (1 self)
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slotted exponential backoff. This paper provides a simple, but nevertheless extremely accurate, analytical model to compute the 802.11 DCF throughput, in the assumption of finite number of terminals and ideal channel conditions. The proposed analysis applies to both the packet transmission schemes
Results 1  10
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878,336