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SMOTE: Synthetic Minority Oversampling Technique
 Journal of Artificial Intelligence Research
, 2002
"... An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often realworld data sets are predominately composed of ``normal'' examples with only a small percentag ..."
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Cited by 634 (27 self)
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percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Undersampling of the majority (normal) class has been proposed as a
SAMPLING TECHNIQUES
"... Sampling is a statistical procedure that involves the selection of a finite number of individuals to represent and infer some knowledge about a population of concern. Sampling techniques are used in a wide range of science and engineering applications; they are of basic importance in computational ..."
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Sampling is a statistical procedure that involves the selection of a finite number of individuals to represent and infer some knowledge about a population of concern. Sampling techniques are used in a wide range of science and engineering applications; they are of basic importance in computational
• Sampling techniques
"... Figure 1: Noisy binary image. The left image displays the a binary image containing no noise. The given noisy image, I0 is shown in the middle. Here 20 % of all pixels are changed. Median filtering is one possibility of removing noise. The result of applying a median filter (3×3 mask) is shown on th ..."
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Figure 1: Noisy binary image. The left image displays the a binary image containing no noise. The given noisy image, I0 is shown in the middle. Here 20 % of all pixels are changed. Median filtering is one possibility of removing noise. The result of applying a median filter (3×3 mask) is shown on the right.
Incorporating nonlocal information into information extraction systems by Gibbs sampling
 IN ACL
, 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
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Cited by 730 (25 self)
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Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling
Cumulated Gainbased Evaluation of IR Techniques
 ACM Transactions on Information Systems
, 2002
"... Modem large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation to the users. In order to develop IR techniques to this direction, i ..."
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Cited by 694 (3 self)
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Modem large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation to the users. In order to develop IR techniques to this direction
Laplacian eigenmaps and spectral techniques for embedding and clustering.
 Proceeding of Neural Information Processing Systems,
, 2001
"... Abstract Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in ..."
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Cited by 668 (7 self)
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Abstract Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded
Sampling Techniques for Kernel Methods
 IN ANNUAL ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14: PROCEEDINGS OF THE 2001 CONFERENCE
, 2001
"... We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we ..."
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Cited by 64 (1 self)
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We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we
Sampling Techniques for the Nyström Method
, 2009
"... The Nyström method is an efficient technique to generate lowrank matrix approximations and is used in several largescale learning applications. A key aspect of this method is the distribution according to which columns are sampled from the original matrix. In this work, we present an analysis of d ..."
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Cited by 33 (4 self)
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The Nyström method is an efficient technique to generate lowrank matrix approximations and is used in several largescale learning applications. A key aspect of this method is the distribution according to which columns are sampled from the original matrix. In this work, we present an analysis
The Fussler Sampling Technique
"... This article discusses a sampling technique suggested by Fussler and further described by Bookstein. We show that Fussier’s method, which takes into account that books vary in thickness, is always better than sampling by length. ..."
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This article discusses a sampling technique suggested by Fussler and further described by Bookstein. We show that Fussier’s method, which takes into account that books vary in thickness, is always better than sampling by length.
Optimally Combining Sampling Techniques for Monte Carlo Rendering
, 1995
"... Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and formfactor computation for radiosity methods. In these cases variance can often be significantly reduced by drawing sa ..."
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Cited by 173 (2 self)
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Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and formfactor computation for radiosity methods. In these cases variance can often be significantly reduced by drawing
Results 1  10
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