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Random vectors in the isotropic position
 J. Funct. Anal
, 1999
"... Abstract. Let y be a random vector in R n, satisfying E y ⊗ y = id. Let M be a natural number and let y1,..., yM be independent copies of y. We prove that for some absolute constant C 1 E ..."
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Cited by 162 (2 self)
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Abstract. Let y be a random vector in R n, satisfying E y ⊗ y = id. Let M be a natural number and let y1,..., yM be independent copies of y. We prove that for some absolute constant C 1 E
Random forests
 Machine Learning
, 2001
"... Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the fo ..."
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Cited by 3613 (2 self)
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Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees
High–Dimensional Random Vectors
, 2013
"... Capturing dependence among a large number of high dimensional random vectors is a very important and challenging problem. By arranging n random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are indep ..."
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Capturing dependence among a large number of high dimensional random vectors is a very important and challenging problem. By arranging n random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors
On the PDF of the Sum of Random Vectors
 IEEE Trans. on Communications
, 2000
"... Abstract — There are various cases in physics and engineering sciences (specially communications) where one requires the envelope PDF of the sum of several random sinusoidal signals. According to the correspondence between a random sinusoidal signal and a random vector, sum of random vectors can be ..."
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Cited by 14 (2 self)
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Abstract — There are various cases in physics and engineering sciences (specially communications) where one requires the envelope PDF of the sum of several random sinusoidal signals. According to the correspondence between a random sinusoidal signal and a random vector, sum of random vectors can
Support Vector Machine Active Learning with Applications to Text Classification
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2001
"... Support vector machines have met with significant success in numerous realworld learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using poolbased acti ..."
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Cited by 735 (5 self)
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Support vector machines have met with significant success in numerous realworld learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool
Fast texture synthesis using treestructured vector quantization
, 2000
"... Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given ..."
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Cited by 561 (12 self)
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Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given
ANALYSIS AND GENERATION OF RANDOM VECTORS WITH COPULAS
"... Copulas are used in finance and insurance for modeling stochastic dependency. They comprehend the entire dependence structure, not only the correlations. Here they are estimated from measured samples of random vectors. The copula and the marginal distributions of the vector elements define a multiva ..."
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Copulas are used in finance and insurance for modeling stochastic dependency. They comprehend the entire dependence structure, not only the correlations. Here they are estimated from measured samples of random vectors. The copula and the marginal distributions of the vector elements define a
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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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
Fast and robust fixedpoint algorithms for independent component analysis
 IEEE TRANS. NEURAL NETW
, 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
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Cited by 884 (34 self)
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Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s
Results 1  10
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7,229