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773
OneClass SVMs for Document Classification
 Journal of Machine Learning Research
, 2001
"... We implemented versions of the SVM appropriate for oneclass classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of oneclass SV ..."
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Cited by 185 (3 self)
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We implemented versions of the SVM appropriate for oneclass classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of oneclass SVM based on identifying “outlier ” data as representative of the secondclass. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tfidf representation and a modification called “Hadamard ” representation. Then we compared it with oneclass versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural oneclass neural network classification method based on “bottleneck” compression generated filters. The SVM approach as represented by Schölkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust.
A comparative study of anomaly detection schemes in network intrusion detection
 In Proceedings of SIAM Conference on Data Mining
, 2003
"... Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induc ..."
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Cited by 173 (8 self)
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Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on a detailed comparative study of several anomaly detection schemes for identifying different network intrusions. Several existing supervised and unsupervised anomaly detection schemes and their variations are evaluated on the DARPA 1998 data set of network connections [9] as well as on real network data using existing standard evaluation techniques as well as using several specific metrics that are appropriate when detecting attacks that involve a large number of connections. Our experimental results indicate that some anomaly detection schemes appear very promising when detecting novel intrusions in both DARPA’98 data and real network data. * 1
Ensemble of ExemplarSVMs for Object Detection and Beyond
"... This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearestneighbor approach. The method is based on training a separate linear SVM classifier for every exemplar ..."
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Cited by 164 (10 self)
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This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearestneighbor approach. The method is based on training a separate linear SVM classifier for every exemplar in the training set. Each of these ExemplarSVMs is thus defined by a single positive instance and millions of negatives. While each detector is quite specific to its exemplar, we empirically observe that an ensemble of such ExemplarSVMs offers surprisingly good generalization. Our performance on the PASCAL VOC detection task is on par with the much more complex latent partbased model of Felzenszwalb et al., at only a modest computational cost increase. But the central benefit of our approach is that it creates an explicit association between each detection and a single training exemplar. Because most detections show good alignment to their associated exemplar, it is possible to transfer any available exemplar metadata (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding. 1.
Support Vector Method for Novelty Detection
, 2000
"... Suppose you are given some dataset drawn from an underlying probability distributionPand you want to estimate a “simple ” subsetSof input space such that the probability that a test point drawn from P lies outside of Sequals some a priori specified between0and1. We propose a m ethod to approach this ..."
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Cited by 164 (4 self)
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Suppose you are given some dataset drawn from an underlying probability distributionPand you want to estimate a “simple ” subsetSof input space such that the probability that a test point drawn from P lies outside of Sequals some a priori specified between0and1. We propose a m ethod to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form offis given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data.
An introduction to boosting and leveraging
 Advanced Lectures on Machine Learning, LNCS
, 2003
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Core vector machines: Fast SVM training on very large data sets
 Journal of Machine Learning Research
, 2005
"... Standard SVM training has O(m 3) time and O(m 2) space complexities, where m is the training set size. It is thus computationally infeasible on very large data sets. By observing that practical SVM implementations only approximate the optimal solution by an iterative strategy, we scale up kernel met ..."
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Cited by 136 (15 self)
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Standard SVM training has O(m 3) time and O(m 2) space complexities, where m is the training set size. It is thus computationally infeasible on very large data sets. By observing that practical SVM implementations only approximate the optimal solution by an iterative strategy, we scale up kernel methods by exploiting such “approximateness ” in this paper. We first show that many kernel methods can be equivalently formulated as minimum enclosing ball (MEB) problems in computational geometry. Then, by adopting an efficient approximate MEB algorithm, we obtain provably approximately optimal solutions with the idea of core sets. Our proposed Core Vector Machine (CVM) algorithm can be used with nonlinear kernels and has a time complexity that is linear in m and a space complexity that is independent of m. Experiments on large toy and realworld data sets demonstrate that the CVM is as accurate as existing SVM implementations, but is much faster and can handle much larger data sets than existing scaleup methods. For example, CVM with the Gaussian kernel produces superior results on the KDDCUP99 intrusion detection data, which has about five million training patterns, in only 1.4 seconds on a 3.2GHz Pentium–4 PC.
The analysis of decomposition methods for support vector machines
 IEEE Transactions on Neural Networks
, 1999
"... Abstract. The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for boundconstrained SVM formulations we demonstrate that the w ..."
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Cited by 134 (21 self)
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Abstract. The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for boundconstrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.
Support Vector Machines: Hype or Hallelujah?
 SIGKDD Explorations
, 2003
"... Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective. The classification ..."
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Cited by 119 (1 self)
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Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective. The classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective. While this overview is not comprehensive, it does provide resources for those interested in further exploring SVMs.
Building Text Classifiers using Positive and Unlabeled Examples
 In Proc. of the ICDM’03
, 2003
"... This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the ..."
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Cited by 116 (17 self)
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This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. In this paper, we first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques. 1.
Learning the Kernel with Hyperkernels
, 2003
"... This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical es ..."
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Cited by 115 (2 self)
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This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem very much akin to the problem of minimizing a regularized risk functional. We state the