Results 1  10
of
250
Online Learning with Kernels
, 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 socalled kernel trick with the large margin idea. There has been little u ..."
Abstract

Cited by 2807 (126 self)
 Add to MetaCart
(Show Context)
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 socalled kernel trick with the large margin idea. There has been little use of these methods in an online setting suitable for realtime 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
Support Vector Machine Active Learning with Applications to Text Classification
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2001
"... Support vector machines have met with significant success in numerous realworld learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using poolbased acti ..."
Abstract

Cited by 729 (5 self)
 Add to MetaCart
Support vector machines have met with significant success in numerous realworld learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using poolbased active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
 In Proc. 18th International Conf. on Machine Learning
, 2001
"... This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expec ..."
Abstract

Cited by 352 (2 self)
 Add to MetaCart
(Show Context)
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible by taking a sampling approach to estimating the expected reduction in error due to the labeling of a query. In experimental results on two realworld data sets we reach high accuracy very quickly, sometimes with four times fewer labeled examples than competing methods. 1.
The Entire Regularization Path for the Support Vector Machine
, 2004
"... The Support Vector Machine is a widely used tool for classification. Many efficient implementations exist for fitting a twoclass SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters. It seems a common practice is to use a ..."
Abstract

Cited by 198 (10 self)
 Add to MetaCart
(Show Context)
The Support Vector Machine is a widely used tool for classification. Many efficient implementations exist for fitting a twoclass SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters. It seems a common practice is to use a default value for the cost parameter, often leading to the least restrictive model. In this paper we argue that the choice of the cost parameter can be critical. We then derive an algorithm that can fit the entire path of SVM solutions for every value of the cost parameter, with essentially the same computational cost as fitting one SVM model. We illustrate our algorithm on some examples, and use our representation to give further insight into the range of SVM solutions.
Sparse online gaussian processes
 Neural Computation
"... Minor corrections included a a The authors acknowledge reader feedbacks We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of ..."
Abstract

Cited by 178 (8 self)
 Add to MetaCart
Minor corrections included a a The authors acknowledge reader feedbacks We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the GP model. By using an appealing parametrisation and projection techniques that use the RKHS norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. This allows both for a propagation of predictions as well as of Bayesian error measures. The significance and robustness of our approach is demonstrated on a variety of experiments. Sparse Online Gaussian Processes 2
Fast Kernel Classifiers With Online And Active Learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Very high dimensional learning systems become theoretically possible when training examples are abundant. The computing cost then becomes the limiting factor. Any efficient learning algorithm should at least take a brief look at each example. But should all examples be given equal attention? This ..."
Abstract

Cited by 153 (18 self)
 Add to MetaCart
Very high dimensional learning systems become theoretically possible when training examples are abundant. The computing cost then becomes the limiting factor. Any efficient learning algorithm should at least take a brief look at each example. But should all examples be given equal attention? This contribution proposes an empirical answer. We first present an online SVM algorithm based on this premise. LASVM yields competitive misclassification rates after a single pass over the training examples, outspeeding stateoftheart SVM solvers. Then we show how active example selection can yield faster training, higher accuracies, and simpler models, using only a fraction of the training example labels.
Online Choice of Active Learning Algorithms
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2004
"... This work is concerned with the question of how to combine online an ensemble of active learners so as to expedite the learning progress in poolbased active learning. We develop an activelearning master algorithm, based on a known competitive algorithm for the multiarmed bandit problem. A major ..."
Abstract

Cited by 115 (2 self)
 Add to MetaCart
This work is concerned with the question of how to combine online an ensemble of active learners so as to expedite the learning progress in poolbased active learning. We develop an activelearning master algorithm, based on a known competitive algorithm for the multiarmed bandit problem. A major challenge in successfully choosing top performing active learners online is to reliably estimate their progress during the learning session. To this end we propose a simple maximum entropy criterion that provides effective estimates in realistic settings. We study the performance of the proposed master algorithm using an ensemble containing two of the best known activelearning algorithms as well as a new algorithm. The resulting
Evaluation of simple performance measures for tuning svm hyperparameters
 Neurocomputing
, 2003
"... www.elsevier.com/locate/neucom ..."
(Show Context)
Classifying Large Data Sets Using SVM with Hierarchical Clusters
 in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2003
"... Support vector machine (SVM) has been a promising method for classification and regression analysis because of its solid mathematical foundation which conveys several salient properties that other methods do not provide. However, despite the prominent properties of SVM, it is not as favored for larg ..."
Abstract

Cited by 71 (3 self)
 Add to MetaCart
(Show Context)
Support vector machine (SVM) has been a promising method for classification and regression analysis because of its solid mathematical foundation which conveys several salient properties that other methods do not provide. However, despite the prominent properties of SVM, it is not as favored for largescale data mining as for pattern recognition or machine learning because the training complexity of SVM is highly dependent on the size of a data set. Many realworld data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform. This paper presents a new method, ClusteringBased SVM (CBSVM), which is specifically designed for handling very large data sets. CBSVM applies a hierarchical microclustering algorithm that scans the entire data set only once to provide an SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. CBSVM tries to generate the best SVM boundary for very large data sets given limited amount of resources. Our experiments on synthetic and real data sets show that CBSVM is highly scalable for very large data sets while also generating high classification accuracy.
Automatically Labeling Video Data Using Multiclass Active Learning
 IN PROCEEDINGS. NINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, 2003
, 2003
"... Labeling video data is an essential prerequisite for many vision applications that depend on training data, such as visual information retrieval, object recognition, and human activity modeling. However, manually creating labels is not only timeconsuming but also subject to human errors, and eventu ..."
Abstract

Cited by 61 (3 self)
 Add to MetaCart
Labeling video data is an essential prerequisite for many vision applications that depend on training data, such as visual information retrieval, object recognition, and human activity modeling. However, manually creating labels is not only timeconsuming but also subject to human errors, and eventually, becomes impossible for a very large amount of data (e.g. 24/7 surveillance video). To minimize the human effort in labeling, we propose a unified multiclass active learning approach for automatically labeling video data. The contributions of this paper include extending active learning from binary classes to multiple classes and evaluating several practical sample selection strategies. The experimental results show that the proposed approach works effectively even with a significantly reduced amount of labeled data. The best sample selection strategy can achieve more than a 50% error reduction over random sample selection.