Estimating the support of a high-dimensional distribution (2001)

by B Schölkopf, J Platt, J Shawe-Taylor, J A Smola, R C Williamson
Venue:Neural Computation
Add To MetaCart
Results 1 - 10 of 784

LIBSVM: A library for support vector machines,”

by Chih-Chung Chang , Chih-Jen Lin - ACM Transactions on Intelligent Systems and Technology, , 2011
"... Abstract LIBSVM is a library for support vector machines (SVM). Its goal is to help users to easily use SVM as a tool. In this document, we present all its implementation details. For the use of LIBSVM, the README file included in the package and the LIBSVM FAQ provide the information. ..."
Abstract - Cited by 6496 (83 self) - Add to MetaCart
Abstract LIBSVM is a library for support vector machines (SVM). Its goal is to help users to easily use SVM as a tool. In this document, we present all its implementation details. For the use of LIBSVM, the README file included in the package and the LIBSVM FAQ provide the information.

Online Learning with Kernels

by Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson , 2003
"... Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little u ..."
Abstract - Cited by 2831 (123 self) - Add to MetaCart
Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little use of these methods in an online setting suitable for real-time applications. In this paper we consider online learning in a Reproducing Kernel Hilbert Space. By considering classical stochastic gradient descent within a feature space, and the use of some straightforward tricks, we develop simple and computationally efficient algorithms for a wide range of problems such as classification, regression, and novelty detection. In addition to allowing the exploitation of the kernel trick in an online setting, we examine the value of large margins for classification in the online setting with a drifting target. We derive worst case loss bounds and moreover we show the convergence of the hypothesis to the minimiser of the regularised risk functional. We present some experimental results that support the theory as well as illustrating the power of the new algorithms for online novelty detection. In addition
(Show Context)

The link-prediction problem for social networks

by David Liben-nowell, Jon Kleinberg - J. AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY , 2007
"... Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a ne ..."
Abstract - Cited by 906 (6 self) - Add to MetaCart
Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
(Show Context)

A tutorial on support vector regression

by Alex J. Smola, Bernhard Schölkopf , 2004
"... In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing ..."
Abstract - Cited by 865 (3 self) - Add to MetaCart
In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

An introduction to kernel-based learning algorithms

by Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf - IEEE TRANSACTIONS ON NEURAL NETWORKS , 2001
"... This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and ..."
Abstract - Cited by 598 (55 self) - Add to MetaCart
This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and

Learning from imbalanced data

by Haibo He, Edwardo A. Garcia - IEEE Trans. on Knowledge and Data Engineering , 2009
"... Abstract—With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-m ..."
Abstract - Cited by 260 (6 self) - Add to MetaCart
Abstract—With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data. Index Terms—Imbalanced learning, classification, sampling methods, cost-sensitive learning, kernel-based learning, active learning, assessment metrics. Ç
(Show Context)

A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data

by Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, Sal Stolfo - Applications of Data Mining in Computer Security , 2002
"... Abstract Most current intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is typically expensive to produce. We present a new geometric framework for unsupervised anomaly detection, which are algorithms that ..."
Abstract - Cited by 238 (9 self) - Add to MetaCart
Abstract Most current intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is typically expensive to produce. We present a new geometric framework for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data. In our framework, data elements are mapped to a feature space which is typically a vector space! d. Anomalies are detected by determining which points lies in sparse
(Show Context)

Editorial: special issue on learning from imbalanced data sets

by Nitesh V. Chawla, Nathalie Japkowicz - SIGKDD Explor. Newsl , 2004
"... The class imbalance problem is one of the (relatively) new problems that emerged when machine learning matured from an embryonic science to an applied technology, amply used in the worlds of business, industry and scientific research. ..."
Abstract - Cited by 216 (5 self) - Add to MetaCart
The class imbalance problem is one of the (relatively) new problems that emerged when machine learning matured from an embryonic science to an applied technology, amply used in the worlds of business, industry and scientific research.
(Show Context)

Support Vector Clustering

by Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik , 2001
"... We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several compo ..."
Abstract - Cited by 215 (1 self) - Add to MetaCart
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We present a simple algorithm for identifying these clusters. The width of the Gaussian kernel controls the scale at which the data is probed while the soft margin constant helps coping with outliers and overlapping clusters. The structure of a dataset is explored by varying the two parameters, maintaining a minimal number of support vectors to assure smooth cluster boundaries. We demonstrate the performance of our algorithm on several datasets.

Correcting sample selection bias by unlabeled data

by Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf
"... 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 ..."
Abstract - Cited by 207 (11 self) - Add to MetaCart
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.
(Show Context)