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1,450,120
Optimal Brain Damage
, 1990
"... We have used informationtheoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved sp ..."
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Cited by 511 (5 self)
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speed of learning and/or classification. The basic idea is to use secondderivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a realworld application.
Minimum Error Rate Training in Statistical Machine Translation
, 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
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Cited by 663 (7 self)
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Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training
Using Discriminant Eigenfeatures for Image Retrieval
, 1996
"... This paper describes the automatic selection of features from an image training set using the theories of multidimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for viewbased class retrieval ..."
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Cited by 504 (15 self)
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This paper describes the automatic selection of features from an image training set using the theories of multidimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for viewbased class
Inductive Learning Algorithms and Representations for Text Categorization
, 1998
"... Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categori ..."
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Cited by 641 (8 self)
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categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs
Active Appearance Models
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and graylevel variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations i ..."
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Cited by 2127 (59 self)
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AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and graylevel variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations
Transductive Inference for Text Classification using Support Vector Machines
, 1999
"... This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimiz ..."
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Cited by 887 (4 self)
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, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs efficiently, handling 10,000 examples and more.
Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
, 2002
"... We describe new algorithms for training tagging models, as an alternative to maximumentropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modific ..."
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Cited by 641 (16 self)
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We describe new algorithms for training tagging models, as an alternative to maximumentropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a
Pegasos: Primal Estimated subgradient solver for SVM
"... We describe and analyze a simple and effective stochastic subgradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
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Cited by 531 (21 self)
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runtime of our method is Õ(d/(λɛ)), where d is a bound on the number of nonzero features in each example. Since the runtime does not depend directly on the size of the training set, the resulting algorithm is especially suited for learning from large datasets. Our approach also extends to non
A bayesian hierarchical model for learning natural scene categories
 In CVPR
, 2005
"... We propose a novel approach to learn and recognize natural scene categories. Unlike previous work [9, 17], it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region ..."
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Cited by 945 (15 self)
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We propose a novel approach to learn and recognize natural scene categories. Unlike previous work [9, 17], it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each
On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
, 2001
"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."
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Cited by 513 (8 self)
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We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size
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