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119
Making LargeScale SVM Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
Abstract

Cited by 1861 (17 self)
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
Abstract

Cited by 628 (1 self)
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
Exploiting CodeRedundancies in ECOC for Reducing its Training Complexity using Incremental and SVM Learners
, 2010
"... We study an approach for speeding up the training of errorcorrecting output codes (ECOC) classifiers. The key idea is to avoid unnecessary computations by exploiting the overlap of the different training sets in the ECOC ensemble. Instead of retraining each classifier from scratch, classifiers tha ..."
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. We experimentally evaluate the algorithm with Hoeffding trees, as an example for incremental learners, where the classifier adaptation is trivial, and with SVMs, where we employ an adaptation strategy based on adapted caching and weight reuse, which guarantees that the learned model
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
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Cited by 561 (20 self)
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generalization error analysis for general output codes with AdaBoost as the binary learner. Experimental results with SVM and AdaBoost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms.
SVM: Reduction of Learning Time
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
Abstract
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
BiasVariance Analysis and Ensembles of SVM
, 2002
"... Accuracy, diversity, and learning characteristics of base learners critically influence the effectiveness of ensemble methods. Biasvariance decomposition of the error can be used as a tool to gain insights into the behavior of learning algorithms, in order to properly design ensemble methods wellt ..."
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Cited by 9 (2 self)
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tuned to the properties of a specific base learner. In this work we analyse biasvariance decomposition of the error in Support Vector Machines (SVM), characterizing it with respect to the kernel and its parameters. We show that the biasvariance decomposition offers a rationale to develop ensemble methods using SVMs
Good Learners for Evil Teachers
"... We consider a supervised machine learning scenario where labels are provided by a heterogeneous set of teachers, some of which are mediocre, incompetent, or perhaps even malicious. We present an algorithm, built on the SVM framework, that explicitly attempts to cope with lowquality and malicious te ..."
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Cited by 34 (2 self)
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We consider a supervised machine learning scenario where labels are provided by a heterogeneous set of teachers, some of which are mediocre, incompetent, or perhaps even malicious. We present an algorithm, built on the SVM framework, that explicitly attempts to cope with lowquality and malicious
Machine Learning manuscript No. (will be inserted by the editor) RandSVM: A Randomized Algorithm for training Support Vector Machines on Large Datasets
, 909
"... Abstract We propose a randomized algorithm for training Support vector machines(SVMs) on large datasets. By using ideas from Random projections we show that the combinatorial dimension of SVMs is O(log n) with high probability. This estimate of combinatorial dimension is used to derive an iterative ..."
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life data sets demonstrate that the algorithm scales up existing SVM learners, without loss of accuracy.
SVMBASED NEGATIVE DATA MINING TO BINARY CLASSIFICATION
, 2006
"... The properties of training data set such as size, distribution and number of attributes significantly contribute to the generalization error of a learning machine. A notwelldistributed data set is prone to lead to a partial overfitting model. The two approaches proposed in this paper for the binar ..."
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algorithm learner by creating one or two additional hypothesis audit and booster to mine the negative examples output from the learner. The learner employs a regular support vector machine to classify main examples and recognize which examples are negative. The audit works on the negative training data
Results 1  10
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119