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9,356
Training Linear SVMs in Linear Time
, 2006
"... Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like text classification, wordsense disambiguation, and drug design. These applications involve a large number of examples n ..."
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Cited by 549 (6 self)
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Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like text classification, wordsense disambiguation, and drug design. These applications involve a large number of examples n
The log of Gravity
 THE REVIEW OF ECONOMICS AND STATISTICS
, 2005
"... Although economists have long been aware of Jensen's inequality, many econometric applications have neglected an important implication of it: the standard practice of interpreting the parameters of loglinearized models estimated by ordinary least squares as elasticities can be highly misleadin ..."
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Cited by 333 (6 self)
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Although economists have long been aware of Jensen's inequality, many econometric applications have neglected an important implication of it: the standard practice of interpreting the parameters of loglinearized models estimated by ordinary least squares as elasticities can be highly
Suffix arrays: A new method for online string searches
, 1991
"... A new and conceptually simple data structure, called a suffix array, for online string searches is introduced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees is that ..."
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Cited by 835 (0 self)
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is that, in practice, they use three to five times less space. From a complexity standpoint, suffix arrays permit online string searches of the type, "Is W a substring of A?" to be answered in time O(P + log N), where P is the length of W and N is the length of A, which is competitive with (and
Data Preparation for Mining World Wide Web Browsing Patterns
 KNOWLEDGE AND INFORMATION SYSTEMS
, 1999
"... The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of tra#c and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An i ..."
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Cited by 567 (43 self)
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is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from
Bayesian Analysis of Stochastic Volatility Models
, 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener alized ARCH ..."
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Cited by 601 (26 self)
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ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and logvolatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility
Attributebased encryption for finegrained access control of encrypted data
 In Proc. of ACMCCS’06
, 2006
"... As more sensitive data is shared and stored by thirdparty sites on the Internet, there will be a need to encrypt data stored at these sites. One drawback of encrypting data, is that it can be selectively shared only at a coarsegrained level (i.e., giving another party your private key). We develop ..."
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Cited by 522 (23 self)
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to decrypt. We demonstrate the applicability of our construction to sharing of auditlog information and broadcast encryption. Our construction supports delegation of private keys which subsumes Hierarchical IdentityBased Encryption (HIBE). E.3 [Data En
The Dantzig selector: statistical estimation when p is much larger than n
, 2005
"... In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n ≪ ..."
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Cited by 879 (14 self)
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In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n
Econometric methods for fractional response variables with an application to 401 (K) plan participation rates
, 1996
"... We develop attractive functional forms and simple quasilikelihood estimation methods for regression models with a fractional dependent variable. Compared with logodds type procedures, there is no difficulty in recovering the regression function for the fractional variable, and there is no need to ..."
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Cited by 472 (8 self)
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We develop attractive functional forms and simple quasilikelihood estimation methods for regression models with a fractional dependent variable. Compared with logodds type procedures, there is no difficulty in recovering the regression function for the fractional variable, and there is no need
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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sequential data.
In particular, the main novel technical contributions of this thesis are as follows: a way of representing
Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T
Applications of Random Sampling in Computational Geometry, II
 Discrete Comput. Geom
, 1995
"... We use random sampling for several new geometric algorithms. The algorithms are "Las Vegas," and their expected bounds are with respect to the random behavior of the algorithms. These algorithms follow from new general results giving sharp bounds for the use of random subsets in geometric ..."
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Cited by 432 (12 self)
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(A + n log n) expected time, where A is the number of intersecting pairs reported. The algorithm requires O(n) space in the worst case. Another algorithm computes the convex hull of n points in E d in O(n log n) expected time for d = 3, and O(n bd=2c ) expected time for d ? 3. The algorithm also
Results 1  10
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9,356