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Proof verification and hardness of approximation problems
 IN PROC. 33RD ANN. IEEE SYMP. ON FOUND. OF COMP. SCI
, 1992
"... We show that every language in NP has a probablistic verifier that checks membership proofs for it using logarithmic number of random bits and by examining a constant number of bits in the proof. If a string is in the language, then there exists a proof such that the verifier accepts with probabilit ..."
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Cited by 797 (39 self)
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the maximum clique size in an Nvertex graph to within a factor of N ɛ is NPhard.
Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement
 QUARTERLY JOURNAL OF ECONOMICS
, 1999
"... The twelfth century rabbinic scholar Maimonides proposed a maximum class size of 40. This same maximum induces a nonlinear and nonmonotonic relationship between grade enrollment and class size in Israeli public schools today. Maimonides’ rule of 40 is used here to construct instrumental variables e ..."
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Cited by 582 (40 self)
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The twelfth century rabbinic scholar Maimonides proposed a maximum class size of 40. This same maximum induces a nonlinear and nonmonotonic relationship between grade enrollment and class size in Israeli public schools today. Maimonides’ rule of 40 is used here to construct instrumental variables
Automatic Subspace Clustering of High Dimensional Data
 Data Mining and Knowledge Discovery
, 2005
"... Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, enduser comprehensibility of the results, nonpresumption of any canonical data distribution, and insensitivity to the or ..."
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Cited by 726 (12 self)
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to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces
Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
 SIAM Journal on Optimization
, 1993
"... We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized to S ..."
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Cited by 548 (12 self)
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mechanical way to algorithms for SDP with proofs of convergence and polynomial time complexity also carrying over in a similar fashion. Finally we study the significance of these results in a variety of combinatorial optimization problems including the general 01 integer programs, the maximum clique
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
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Cited by 692 (4 self)
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rigor. ii 1 Maximumlikelihood Recall the definition of the maximumlikelihood estimation problem. We have a density function ¢¡¤£¦ ¥ §© ¨ that is governed by the set of parameters § (e.g., might be a set of Gaussians and § could be the means and covariances). We also have a data set of size
Boosting the margin: A new explanation for the effectiveness of voting methods
 IN PROCEEDINGS INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1997
"... One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this ..."
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Cited by 894 (52 self)
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One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show
Scalable Application Layer Multicast
, 2002
"... We describe a new scalable applicationlayer multicast protocol, specifically designed for lowbandwidth, data streaming applications with large receiver sets. Our scheme is based upon a hierarchical clustering of the applicationlayer multicast peers and can support a number of different data deliv ..."
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Cited by 732 (21 self)
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sites. In our experiments, averagegroup members established and maintained lowlatency paths and incurred a maximum packet loss rate of less than 1 % as members randomly joined and left the multicast group. The average control overhead during our experiments was less than 1 Kbps for groups of size 100.
Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification
 Psychological Methods
, 1998
"... This study evaluated the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distributionfree (ADF)based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic robustn ..."
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Cited by 538 (0 self)
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This study evaluated the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distributionfree (ADF)based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic
Estimation of probabilities from sparse data for the language model component of a speech recognizer
 IEEE Transactions on Acoustics, Speech and Signal Processing
, 1987
"... AbstractThe description of a novel type of rngram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods. Wh ..."
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Cited by 798 (2 self)
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, and it is a problem that one always encounters while collecting frequency statistics on words and word sequences (mgrams) from a text of finite size. This means that even for a very large data collection, the maximum likelihood estimation method does not allow Turing’s estimate PT for a probability of a
Combining Branch Predictors
, 1993
"... One of the key factors determining computer performance is the degree to which the implementation can take advantage of instructionlevel parallelism. Perhaps the most critical limit to this parallelism is the presence of conditional branches that determine which instructions need to be executed ne ..."
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Cited by 630 (0 self)
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of branch history by hashing it together with the branch address. Together, these new techniques are shown to outperform previously known approaches both in terms of maximum prediction accuracy and the prediction accuracy for a given predictor size. Specifically, prediction accuracy reaches 98.1% correct
Results 1  10
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1,875,737