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Random Oracles are Practical: A Paradigm for Designing Efficient Protocols
, 1995
"... We argue that the random oracle model  where all parties have access to a public random oracle  provides a bridge between cryptographic theory and cryptographic practice. In the paradigm we suggest, a practical protocol P is produced by first devising and proving correct a protocol P R for the ..."
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Cited by 1643 (75 self)
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for the random oracle model, and then replacing oracle accesses by the computation of an "appropriately chosen" function h. This paradigm yields protocols much more efficient than standard ones while retaining many of the advantages of provable security. We illustrate these gains for problems including
Introduction to redundant arrays of inexpensive disks
 Proceedings of the IEEE COMPCON
, 1989
"... Abstract Increasmg performance of CPUs and memorres wrll be squandered lf not matched by a sunrlm peformance ourease m II0 Whde the capactty of Smgle Large Expenstve D&T (SLED) has grown rapuily, the performance rmprovement of SLED has been modest Redundant Arrays of Inexpensive Disks (RAID), ba ..."
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Cited by 846 (55 self)
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Abstract Increasmg performance of CPUs and memorres wrll be squandered lf not matched by a sunrlm peformance ourease m II0 Whde the capactty of Smgle Large Expenstve D&T (SLED) has grown rapuily, the performance rmprovement of SLED has been modest Redundant Arrays of Inexpensive Disks (RAID
Short Signatures without Random Oracles
, 2004
"... We describe a short signature scheme which is existentially unforgeable under a chosen message attack without using random oracles. The security of our scheme depends on a new complexity assumption we call the Strong Di#eHellman assumption. This assumption has similar properties to the Strong RS ..."
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Cited by 387 (13 self)
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RSA assumption, hence the name. Strong RSA was previously used to construct signature schemes without random oracles. However, signatures generated by our scheme are much shorter and simpler than signatures from schemes based on Strong RSA.
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
, 2001
"... Variable selection is fundamental to highdimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
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Cited by 914 (61 self)
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Variable selection is fundamental to highdimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized
Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
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Cited by 664 (14 self)
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introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are nonMarkovian and have a large number of parameters that must be estimated. Relations to other learning approaches, including decision trees, are given. As a demonstration
PseudoRandom Generation from OneWay Functions
 PROC. 20TH STOC
, 1988
"... Pseudorandom generators are fundamental to many theoretical and applied aspects of computing. We show howto construct a pseudorandom generator from any oneway function. Since it is easy to construct a oneway function from a pseudorandom generator, this result shows that there is a pseudorandom gene ..."
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Cited by 887 (22 self)
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Pseudorandom generators are fundamental to many theoretical and applied aspects of computing. We show howto construct a pseudorandom generator from any oneway function. Since it is easy to construct a oneway function from a pseudorandom generator, this result shows that there is a pseudorandom
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 515 (18 self)
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. A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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law), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball
Simulating Physics with Computers
 SIAM Journal on Computing
, 1982
"... A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time of at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. ..."
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Cited by 601 (1 self)
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. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum
Feature selection based on mutual information: Criteria of maxdepe ndency, maxrelevance, and minredundancy
 IEEE Trans. Pattern Analysis and Machine Intelligence
"... Abstract—Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we f ..."
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Cited by 533 (7 self)
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first derive an equivalent form, called minimalredundancymaximalrelevance criterion (mRMR), for firstorder incremental feature selection. Then, we present a twostage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us
Results 1  10
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1,150,807