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866
Estimating the Support of a High-Dimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 783 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is 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. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a preliminary 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 d...
An introduction to kernel-based learning algorithms
- IEEE TRANSACTIONS ON NEURAL NETWORKS
, 2001
"... This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and ..."
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Cited by 598 (55 self)
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This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
- In Proc. 43st ACL
, 2005
"... We address the rating-inference problem, wherein rather than simply decide whether a review is “thumbs up ” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an int ..."
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Cited by 298 (2 self)
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We address the rating-inference problem, wherein rather than simply decide whether a review is “thumbs up ” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, “three stars ” is intuitively closer to “four stars ” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labeling formulation of the problem, that alters a given-ary classifier’s output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem. 1
Recovering 3D Human Pose from Monocular Images
"... We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descrip ..."
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Cited by 261 (0 self)
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We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. We evaluate several different regression methods: ridge regression, Relevance Vector Machine (RVM) regression and Support Vector Machine (SVM) regression over both linear and kernel bases. The RVMs provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. Loss of depth and limb labelling information often makes the recovery of 3D pose from single silhouettes ambiguous. We propose two solutions to this: the first embeds the method in a tracking framework, using dynamics from the previous state estimate to disambiguate the pose; the second uses a mixture of regressors framework to return multiple solutions for each silhouette. We show that the resulting system tracks long sequences stably, and is also capable of accurately reconstructing 3D human pose from single images, giving multiple possible solutions in ambiguous cases. For realism and good generalization over a wide range of viewpoints, we train the regressors on images resynthesized from real human motion capture data. The method is demonstrated on a 54-parameter full body pose model, both quantitatively on independent but similar test data, and qualitatively on real image sequences. Mean angular errors of 4–5 degrees are obtained — a factor of 3 better than the current state of the art for the much simpler upper body problem.
Sparse representation for color image restoration
- the IEEE Trans. on Image Processing
, 2007
"... Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted ..."
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Cited by 219 (30 self)
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Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task [1], and shown to perform very well for various gray-scale image processing tasks. In this paper we address the problem of learning dictionaries for color images and extend the K-SVD-based gray-scale image denoising algorithm that appears in [2]. This work puts forward ways for handling non-homogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper. EDICS Category: COL-COLR (Color processing) I.
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 scale-up methods. For example, CVM with the Gaussian kernel produces superior results on the KDDCUP-99 intrusion detection data, which has about five million training patterns, in only 1.4 seconds on a 3.2GHz Pentium–4 PC.
He says, she says: conflict and coordination in Wikipedia.
- Proc. CHI‟07,
, 2007
"... ABSTRACT Wikipedia, a wiki-based encyclopedia, has become one of the most successful experiments in collaborative knowledge building on the Internet. As Wikipedia continues to grow, the potential for conflict and the need for coordination increase as well. This article examines the growth of such n ..."
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Cited by 123 (10 self)
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ABSTRACT Wikipedia, a wiki-based encyclopedia, has become one of the most successful experiments in collaborative knowledge building on the Internet. As Wikipedia continues to grow, the potential for conflict and the need for coordination increase as well. This article examines the growth of such non-direct work and describes the development of tools to characterize conflict and coordination costs in Wikipedia. The results may inform the design of new collaborative knowledge systems.
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.
Latent Semantic Kernels
"... Kernel methods like Support Vector Machines have successfully been used for text categorization. A standard choice of kernel function has been the inner product between the vector-space representationoftwo documents, in analogy with classical information retrieval (IR) approaches. Latent Semantic In ..."
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Cited by 114 (9 self)
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Kernel methods like Support Vector Machines have successfully been used for text categorization. A standard choice of kernel function has been the inner product between the vector-space representationoftwo documents, in analogy with classical information retrieval (IR) approaches. Latent Semantic Indexing (LSI) has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between two documents. One of its main drawbacks, in IR, is its computational cost. In this paper we describe how the LSI approach can be implementedinakernel-de ned feature space. We provide experimental results demonstrating that the approach can significantly improve performance, and that it does not impair it.
Practical selection of SVM parameters and noise estimation for SVM regression”, Neural
- Netw
, 2004
"... Abstract We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, 1-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approac ..."
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Cited by 112 (1 self)
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Abstract We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, 1-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications. In particular, we describe a new analytical prescription for setting the value of insensitive zone 1; as a function of training sample size. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low-and high-dimensional regression problems. Further, we point out the importance of Vapnik's 1-insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (using proposed selection of 1-values) with regression using 'least-modulus' loss ð1 ¼ 0Þ and standard squared loss. These comparisons indicate superior generalization performance of SVM regression under sparse sample settings, for various types of additive noise. q
