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62
A support vector method for multivariate performance measures
 Proceedings of the 22nd International Conference on Machine Learning
, 2005
"... This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially nonlinear per ..."
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Cited by 192 (5 self)
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This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially nonlinear performance measures, in particular ROCArea and all measures that can be computed from the contingency table. The conventional classification SVM arises as a special case of our method. 1.
Multicategory Support Vector Machines, theory, and application to the classification of microarray data and satellite radiance data
 Journal of the American Statistical Association
, 2004
"... Twocategory support vector machines (SVM) have been very popular in the machine learning community for classi � cation problems. Solving multicategory problems by a series of binary classi � ers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We pro ..."
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Cited by 175 (17 self)
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Twocategory support vector machines (SVM) have been very popular in the machine learning community for classi � cation problems. Solving multicategory problems by a series of binary classi � ers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We propose the multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case and has good theoretical properties. The proposed method provides a unifying framework when there are either equal or unequal misclassi � cation costs. As a tuning criterion for the MSVM, an approximate leaveoneout crossvalidation function, called Generalized Approximate Cross Validation, is derived, analogous to the binary case. The effectiveness of the MSVM is demonstrated through the applications to cancer classi � cation using microarray data and cloud classi � cation with satellite radiance pro � les.
Correcting sample selection bias by unlabeled data
"... We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We prese ..."
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Cited by 129 (9 self)
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We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.
A support vector method for optimizing average precision
 In Proceedings of SIGIR’07
, 2007
"... Machine learning is commonly used to improve ranked retrieval systems. Due to computational difficulties, few learning techniques have been developed to directly optimize for mean average precision (MAP), despite its widespread use in evaluating such systems. Existing approaches optimizing MAP eithe ..."
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Cited by 113 (5 self)
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Machine learning is commonly used to improve ranked retrieval systems. Due to computational difficulties, few learning techniques have been developed to directly optimize for mean average precision (MAP), despite its widespread use in evaluating such systems. Existing approaches optimizing MAP either do not find a globally optimal solution, or are computationally expensive. In contrast, we present a general SVM learning algorithm that efficiently finds a globally optimal solution to a straightforward relaxation of MAP. We evaluate our approach using the TREC 9 and TREC 10 Web Track corpora (WT10g), comparing against SVMs optimized for accuracy and ROCArea. In most cases we show our method to produce statistically significant improvements in MAP scores.
Everything Old Is New Again: A Fresh Look at Historical Approaches
 in Machine Learning. PhD thesis, MIT
, 2002
"... 2 Everything Old Is New Again: A Fresh Look at Historical ..."
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Cited by 88 (6 self)
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2 Everything Old Is New Again: A Fresh Look at Historical
Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2002
"... Monitoring gene expression profiles is a novel approach in cancer diagnosis. Several studies showed that prediction of cancer types using gene expression data is promising and very informative. The Support Vector Machine (SVM) is one of the classification methods successfully applied to the cancer d ..."
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Cited by 88 (4 self)
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Monitoring gene expression profiles is a novel approach in cancer diagnosis. Several studies showed that prediction of cancer types using gene expression data is promising and very informative. The Support Vector Machine (SVM) is one of the classification methods successfully applied to the cancer diagnosis problems using gene expression data. However, its optimal extension to more than two classes was not obvious, which might impose limitations in its application to multiple tumor types. In this paper, we analyze a couple of published multiple cancer types data sets by the multicategory SVM, which is a recently proposed extension of the binary SVM.
Support vector machines and the Bayes rule in classification
 Data Mining Knowledge Disc
, 2002
"... Abstract. The Bayes rule is the optimal classification rule if the underlying distribution of the data is known. In practice we do not know the underlying distribution, and need to “learn ” classification rules from the data. One way to derive classification rules in practice is to implement the Bay ..."
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Cited by 83 (13 self)
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Abstract. The Bayes rule is the optimal classification rule if the underlying distribution of the data is known. In practice we do not know the underlying distribution, and need to “learn ” classification rules from the data. One way to derive classification rules in practice is to implement the Bayes rule approximately by estimating an appropriate classification function. Traditional statistical methods use estimated log odds ratio as the classification function. Support vector machines (SVMs) are one type of large margin classifier, and the relationship between SVMs and the Bayes rule was not clear. In this paper, it is shown that the asymptotic target of SVMs are some interesting classification functions that are directly related to the Bayes rule. The rate of convergence of the solutions of SVMs to their corresponding target functions is explicitly established in the case of SVMs with quadratic or higher order loss functions and spline kernels. Simulations are given to illustrate the relation between SVMs and the Bayes rule in other cases. This helps understand the success of SVMs in many classification studies, and makes it easier to compare SVMs and traditional statistical methods.
Statistically undetectable JPEG steganography: Dead ends, challenges, and opportunities
 Proceedings of the 9th ACM Multimedia & Security Workshop
, 2007
"... The goal of this paper is to determine the steganographic capacity of JPEG images (the largest payload that can be undetectably embedded) with respect to current best steganalytic methods. Additionally, by testing selected steganographic algorithms we evaluate the influence of specific design elemen ..."
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Cited by 33 (18 self)
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The goal of this paper is to determine the steganographic capacity of JPEG images (the largest payload that can be undetectably embedded) with respect to current best steganalytic methods. Additionally, by testing selected steganographic algorithms we evaluate the influence of specific design elements and principles, such as the choice of the JPEG compressor, matrix embedding, adaptive contentdependent selection channels, and minimal distortion steganography using side information at the sender. From our experiments, we conclude that the average steganographic capacity of grayscale JPEG images with quality factor 70 is approximately 0.05 bits per nonzero AC DCT coefficient.
Classboundary alignment for imbalanced dataset learning
 In ICML 2003 Workshop on Learning from Imbalanced Data Sets
, 2003
"... In this paper, we propose the classboundaryalignment algorithm to augment SVMs to deal with imbalanced trainingdata problems posed by many emerging applications (e.g., image retrieval, video surveillance, and gene profiling). Through a simple example, we first show that SVMs can be ineffective in ..."
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Cited by 27 (1 self)
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In this paper, we propose the classboundaryalignment algorithm to augment SVMs to deal with imbalanced trainingdata problems posed by many emerging applications (e.g., image retrieval, video surveillance, and gene profiling). Through a simple example, we first show that SVMs can be ineffective in determining the class boundary when the training instances of the target class are heavily outnumbered by the nontarget training instances. To remedy this problem, we propose to adjust the class boundary either by transforming the kernel function when the training data can be represented in a vector space, or by modifying the kernel matrix when the data do not have a vectorspace representation (e.g., sequence data). Through theoretical analysis and empirical study, we show that the classboundaryalignment algorithm works effectively with images (data that have a vectorspace representation) and video sequences (data that do not have a vectorspace representation). 1.
Adaptive featurespace conformal transformation for imbalanceddata learning
 in The Twentieth International Conference on Machine Learning (ICML2003), (Washington DC
, 2003
"... When the training instances of the target class are heavily outnumbered by nontarget training instances, SVMs can be ineffective in determining the class boundary. To remedy this problem, we propose an adaptive conformal transformation (ACT) algorithm. ACT considers featurespace distance and the c ..."
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Cited by 20 (9 self)
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When the training instances of the target class are heavily outnumbered by nontarget training instances, SVMs can be ineffective in determining the class boundary. To remedy this problem, we propose an adaptive conformal transformation (ACT) algorithm. ACT considers featurespace distance and the classimbalance ratio when it performs conformal transformation on a kernel function. Experimental results on UCI and realworld datasets show ACT to be effective in improving class prediction accuracy. 1.