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157
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 so-called kernel trick with the large margin idea. There has been little u ..."
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Cited by 2831 (123 self)
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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
Large Margin Classification Using the Perceptron Algorithm
- Machine Learning
, 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik 's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
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Cited by 521 (2 self)
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We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik 's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem, while saving significantly on computation time and programming effort. 1 Introduction One of the most influential developments in the theory of machine learning in the last few years is Vapnik's work on supp...
Online passive-aggressive algorithms
- JMLR
, 2006
"... We present a unified view for online classification, regression, and uniclass problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. The end result is new alg ..."
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Cited by 435 (24 self)
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We present a unified view for online classification, regression, and uniclass problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. The end result is new algorithms and accompanying loss bounds for hinge-loss regression and uniclass. We also get refined loss bounds for previously studied classification algorithms.
Fast Binary Feature Selection with Conditional Mutual Information
- Journal of Machine Learning Research
, 2004
"... We propose in this paper a very fast feature selection technique based on conditional mutual information. ..."
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Cited by 176 (1 self)
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We propose in this paper a very fast feature selection technique based on conditional mutual information.
On the Generalization Ability of On-line Learning Algorithms
- IEEE Transactions on Information Theory
, 2001
"... In this paper we show that on-line algorithms for classification and regression can be naturally used to obtain hypotheses with good datadependent tail bounds on their risk. Our results are proven without requiring complicated concentration-of-measure arguments and they hold for arbitrary on-lin ..."
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Cited by 176 (7 self)
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In this paper we show that on-line algorithms for classification and regression can be naturally used to obtain hypotheses with good datadependent tail bounds on their risk. Our results are proven without requiring complicated concentration-of-measure arguments and they hold for arbitrary on-line learning algorithms. Furthermore, when applied to concrete on-line algorithms, our results yield tail bounds that in many cases are comparable or better than the best known bounds.
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 ..."
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Cited by 153 (18 self)
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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 state-of-the-art 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.
A second-order perceptron algorithm
, 2005
"... Kernel-based linear-threshold algorithms, such as support vector machines and Perceptron-like algorithms, are among the best available techniques for solving pattern classification problems. In this paper, we describe an extension of the classical Perceptron algorithm, called second-order Perceptr ..."
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Cited by 83 (23 self)
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Kernel-based linear-threshold algorithms, such as support vector machines and Perceptron-like algorithms, are among the best available techniques for solving pattern classification problems. In this paper, we describe an extension of the classical Perceptron algorithm, called second-order Perceptron, and analyze its performance within the mistake bound model of on-line learning. The bound achieved by our algorithm depends on the sensitivity to second-order data information and is the best known mistake bound for (efficient) kernel-based linear-threshold classifiers to date. This mistake bound, which strictly generalizes the well-known Perceptron bound, is expressed in terms of the eigenvalues of the empirical data correlation matrix and depends on a parameter controlling the sensitivity of the algorithm to the distribution of these eigenvalues. Since the optimal setting of this parameter is not known a priori, we also analyze two variants of the second-order Perceptron algorithm: one that adaptively sets the value of the parameter in terms of the number of mistakes made so far, and one that is parameterless, based on pseudoinverses.
Online Bayes Point Machines
"... We present a new and simple algorithm for learning large margin classi ers that works in a truly online manner. The algorithm generates a linear classi er by averaging the weights associated with several perceptron-like algorithms run in parallel in order to approximate the Bayes point. A rand ..."
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Cited by 83 (3 self)
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We present a new and simple algorithm for learning large margin classi ers that works in a truly online manner. The algorithm generates a linear classi er by averaging the weights associated with several perceptron-like algorithms run in parallel in order to approximate the Bayes point. A random subsample of the incoming data stream is used to ensure diversity in the perceptron solutions. We experimentally study the algorithm's performance on online and batch learning settings.
Kernel methods for relational learning
, 2003
"... Kernel methods have gained a great deal of popularity in the machine learning commu-nity as a method to learn indirectly in high-dimensional feature spaces. Those interested in relational learning have recently begun to cast learning from structured and relational data in terms of kernel operations. ..."
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Cited by 79 (4 self)
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Kernel methods have gained a great deal of popularity in the machine learning commu-nity as a method to learn indirectly in high-dimensional feature spaces. Those interested in relational learning have recently begun to cast learning from structured and relational data in terms of kernel operations. We describe a general family of kernel func-tions built up from a description language of limited expressivity and use it to study the bene ts and drawbacks of kernel learning in relational domains. Learning with kernels in this family directly models learning over an expanded feature space constructed using the same description language. This allows us to examine issues of time complexity in terms of learning with these and other relational ker-nels, and how these relate to generalization ability. The tradeos between using kernels in a very high dimensional implicit space ver-sus a restricted feature space, is highlighted through two experiments, in bioinformatics and in natural language processing. 1.
Distributed Training Strategies for the Structured Perceptron
"... Perceptron training is widely applied in the natural language processing community for learning complex structured models. Like all structured prediction learning frameworks, the structured perceptron can be costly to train as training complexity is proportional to inference, which is frequently non ..."
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Cited by 77 (4 self)
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Perceptron training is widely applied in the natural language processing community for learning complex structured models. Like all structured prediction learning frameworks, the structured perceptron can be costly to train as training complexity is proportional to inference, which is frequently non-linear in example sequence length. In this paper we investigate distributed training strategies for the structured perceptron as a means to reduce training times when computing clusters are available. We look at two strategies and provide convergence bounds for a particular mode of distributed structured perceptron training based on iterative parameter mixing (or averaging). We present experiments on two structured prediction problems – namedentity recognition and dependency parsing – to highlight the efficiency of this method. 1
