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42
An introduction to boosting and leveraging
 Advanced Lectures on Machine Learning, LNCS
, 2003
"... ..."
MistakeDriven Learning in Text Categorization
 IN EMNLP97, THE SECOND CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING
, 1997
"... Learning problems in the text processing domain often map the text to a space whose dimensions are the measured fea tures of the text, e.g., its words. Three characteristic properties of this domain are (a) very high dimensionality, (b) both the learned concepts and the instances reside very ..."
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Cited by 98 (9 self)
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Learning problems in the text processing domain often map the text to a space whose dimensions are the measured fea tures of the text, e.g., its words. Three characteristic properties of this domain are (a) very high dimensionality, (b) both the learned concepts and the instances reside very sparsely in the feature space, and (c) a high variation in the number of active features in an instance. In this work we study three mistakedriven learning algo rithms for a typical task of this nature  text categorization. We argue
A New Approximate Maximal Margin Classification Algorithm
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2001
"... A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called alma p (Approximate Large Margin algorithm w.r.t. norm p), takes O (p 1) 2 2 corrections to separate the data wi ..."
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Cited by 87 (5 self)
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A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called alma p (Approximate Large Margin algorithm w.r.t. norm p), takes O (p 1) 2 2 corrections to separate the data with pnorm margin larger than (1 ) , where is the (normalized) pnorm margin of the data. alma p avoids quadratic (or higherorder) programming methods. It is very easy to implement and is as fast as online algorithms, such as Rosenblatt's Perceptron algorithm. We performed extensive experiments on both realworld and artificial datasets. We compared alma 2 (i.e., alma p with p = 2) to standard Support vector Machines (SVM) and to two incremental algorithms: the Perceptron algorithm and Li and Long's ROMMA. The accuracy levels achieved by alma 2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, alma 2 is quite faster and easier to implement than standard SVM training algorithms. When learning sparse target vectors, alma p with p > 2 largely outperforms Perceptronlike algorithms, such as alma 2 .
General convergence results for linear discriminant updates
 Machine Learning
, 1997
"... Abstract. The problem of learning lineardiscriminant concepts can be solved by various mistakedriven update procedures, including the Winnow family of algorithms and the wellknown Perceptron algorithm. In this paper we define the general class of “quasiadditive ” algorithms, which includes Perce ..."
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Cited by 83 (0 self)
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Abstract. The problem of learning lineardiscriminant concepts can be solved by various mistakedriven update procedures, including the Winnow family of algorithms and the wellknown Perceptron algorithm. In this paper we define the general class of “quasiadditive ” algorithms, which includes Perceptron and Winnow as special cases. We give a single proof of convergence that covers a broad subset of algorithms in this class, including both Perceptron and Winnow, but also many new algorithms. Our proof hinges on analyzing a generic measure of progress construction that gives insight as to when and how such algorithms converge. Our measure of progress construction also permits us to obtain good mistake bounds for individual algorithms. We apply our unified analysis to new algorithms as well as existing algorithms. When applied to known algorithms, our method “automatically ” produces close variants of existing proofs (recovering similar bounds)—thus showing that, in a certain sense, these seemingly diverse results are fundamentally isomorphic. However, we also demonstrate that the unifying principles are more broadly applicable, and analyze a new class of algorithms that smoothly interpolate between the additiveupdate behavior of Perceptron and the multiplicativeupdate behavior of Winnow.
Tracking the Best Disjunction
 Machine Learning
, 1995
"... . Littlestone developed a simple deterministic online learning algorithm for learning kliteral disjunctions. This algorithm (called Winnow) keeps one weight for each of the n variables and does multiplicative updates to its weights. We develop a randomized version of Winnow and prove bounds for a ..."
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Cited by 72 (11 self)
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. Littlestone developed a simple deterministic online learning algorithm for learning kliteral disjunctions. This algorithm (called Winnow) keeps one weight for each of the n variables and does multiplicative updates to its weights. We develop a randomized version of Winnow and prove bounds for an adaptation of the algorithm for the case when the disjunction may change over time. In this case a possible target disjunction schedule T is a sequence of disjunctions (one per trial) and the shift size is the total number of literals that are added/removed from the disjunctions as one progresses through the sequence. We develop an algorithm that predicts nearly as well as the best disjunction schedule for an arbitrary sequence of examples. This algorithm that allows us to track the predictions of the best disjunction is hardly more complex than the original version. However the amortized analysis needed for obtaining worstcase mistake bounds requires new techniques. In some cases our low...
Relative Loss Bounds for Multidimensional Regression Problems
 MACHINE LEARNING
, 2001
"... We study online generalized linear regression with multidimensional outputs, i.e., neural networks with multiple output nodes but no hidden nodes. We allow at the final layer transfer functions such as the softmax function that need to consider the linear activations to all the output neurons. The ..."
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Cited by 72 (12 self)
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We study online generalized linear regression with multidimensional outputs, i.e., neural networks with multiple output nodes but no hidden nodes. We allow at the final layer transfer functions such as the softmax function that need to consider the linear activations to all the output neurons. The weight vectors used to produce the linear activations are represented indirectly by maintaining separate parameter vectors. We get the weight vector by applying a particular parameterization function to the parameter vector. Updating the parameter vectors upon seeing new examples is done additively, as in the usual gradient descent update. However, by using a nonlinear parameterization function between the parameter vectors and the weight vectors, we can make the resulting update of the weight vector quite different from a true gradient descent update. To analyse such updates, we define a notion of a matching loss function and apply it both to the transfer function and to the parameterization function. The loss function that matches the transfer function is used to measure the goodness of the predictions of the algorithm. The loss function that matches the parameterization function can be used both as a measure of divergence between models in motivating the update rule of the algorithm and as a measure of progress in analyzing its relative performance compared to an arbitrary fixed model. As a result, we have a unified treatment that generalizes earlier results for the gradient descent and exponentiated gradient algorithms to multidimensional outputs, including multiclass logistic regression.
Path Kernels and Multiplicative Updates
 Journal of Machine Learning Research
, 2003
"... Kernels are typically applied to linear algorithms whose weight vector is a linear combination of the feature vectors of the examples. Online versions of these algorithms are sometimes called "additive updates" because they add a multiple of the last feature vector to the current weight vector. ..."
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Cited by 63 (6 self)
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Kernels are typically applied to linear algorithms whose weight vector is a linear combination of the feature vectors of the examples. Online versions of these algorithms are sometimes called "additive updates" because they add a multiple of the last feature vector to the current weight vector.
Learning a Semantic Space From User’s Relevance Feedback for Image Retrieval
 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
, 2003
"... As current methods for contentbased retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user’s relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interact ..."
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Cited by 60 (3 self)
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As current methods for contentbased retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user’s relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the longterm learning process, we also model the traditional approaches to query refinement using relevance feedback as a shortterm learning process. The proposed shortand longterm learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.
A secondorder perceptron algorithm
, 2005
"... Kernelbased linearthreshold algorithms, such as support vector machines and Perceptronlike 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 secondorder Perceptr ..."
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Cited by 57 (20 self)
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Kernelbased linearthreshold algorithms, such as support vector machines and Perceptronlike 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 secondorder Perceptron, and analyze its performance within the mistake bound model of online learning. The bound achieved by our algorithm depends on the sensitivity to secondorder data information and is the best known mistake bound for (efficient) kernelbased linearthreshold classifiers to date. This mistake bound, which strictly generalizes the wellknown 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 secondorder 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.
Linear Hinge Loss and Average Margin
, 1998
"... We describe a unifying method for proving relative loss bounds for online linear threshold classification algorithms, such as the Perceptron and the Winnow algorithms. For classification problems the discrete loss is used, i.e., the total number of prediction mistakes. We introduce a continuous ..."
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Cited by 38 (11 self)
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We describe a unifying method for proving relative loss bounds for online linear threshold classification algorithms, such as the Perceptron and the Winnow algorithms. For classification problems the discrete loss is used, i.e., the total number of prediction mistakes. We introduce a continuous loss function, called the "linear hinge loss", that can be employed to derive the updates of the algorithms. We first prove bounds w.r.t. the linear hinge loss and then convert them to the discrete loss. We introduce a notion of "average margin" of a set of examples . We show how relative loss bounds based on the linear hinge loss can be converted to relative loss bounds i.t.o. the discrete loss using the average margin.