Results 1  10
of
27,316
Online largemargin training of dependency parsers
 In Proc. ACL
, 2005
"... We present an effective training algorithm for linearlyscored dependency parsers that implements online largemargin multiclass training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a competi ..."
Abstract

Cited by 306 (23 self)
 Add to MetaCart
We present an effective training algorithm for linearlyscored dependency parsers that implements online largemargin multiclass training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a
Online largemargin training for statistical machine translation
 In Proc. of EMNLP
, 2007
"... We achieved a state of the art performance in statistical machine translation by using a large number of features with an online largemargin training algorithm. The millions of parameters were tuned only on a small development set consisting of less than 1K sentences. Experiments on ArabictoEngli ..."
Abstract

Cited by 36 (3 self)
 Add to MetaCart
We achieved a state of the art performance in statistical machine translation by using a large number of features with an online largemargin training algorithm. The millions of parameters were tuned only on a small development set consisting of less than 1K sentences. Experiments on Arabic
Adaptive Large Margin Training for Multilabel Classification
"... Multilabel classification is a central problem in many areas of data analysis, including text and multimedia categorization, where individual data objects need to be assigned multiple labels. A key challenge in these tasks is to learn a classifier that can properly exploit label correlations without ..."
Abstract

Cited by 12 (9 self)
 Add to MetaCart
without requiring exponential enumeration of label subsets during training or testing. We investigate novel loss functions for multilabel training within a large margin framework—identifying a simple alternative that yields improved generalization while still allowing efficient training. We furthermore
Online LargeMargin Training of Syntactic and Structural Translation Features
"... Minimumerrorrate training (MERT) is a bottleneck for current development in statistical machine translation because it is limited in the number of weights it can reliably optimize. Building on the work of Watanabe et al., we explore the use of the MIRA algorithm of Crammer et al. as an alternative ..."
Abstract

Cited by 124 (12 self)
 Add to MetaCart
in the Hiero hierarchical phrasebased model: first, we simultaneously train a large number of Marton and Resnik’s soft syntactic constraints, and, second, we introduce a novel structural distortion model. In both cases we obtain significant improvements in translation performance. Optimizing them
Large margin training of continuous density hidden Markov models
 in Automatic Speech and Speaker Recognition: Large Margin and Kernel
, 2009
"... Abstract. Continuous density hidden Markov models (CDHMMs) are an essential component of modern systems for automatic speech recognition (ASR). These models assign probabilities to the sequences of acoustic feature vectors extracted by signal processing of speech waveforms. In this chapter, we inve ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
improvements in performance have been obtained by discriminative training of large margin classifiers. Building on both these lines of work, we show how to train CDHMMs by maximizing an appropriately defined margin between correct and incorrect decodings of speech waveforms. We start by defining an objective
A fast online algorithm for large margin training of continuous density hidden markov models
 in Proceedings of Interspeech2009
, 2009
"... We propose an online learning algorithm for large margin training of continuous density hidden Markov models. The online algorithm updates the model parameters incrementally after the decoding of each training utterance. For large margin training, the algorithm attempts to separate the loglikelihoo ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
We propose an online learning algorithm for large margin training of continuous density hidden Markov models. The online algorithm updates the model parameters incrementally after the decoding of each training utterance. For large margin training, the algorithm attempts to separate the log
Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models
 In Proceedings of ICASSP 2007
, 2007
"... In this paper we compare three frameworks for discriminative training of continuousdensity hidden Markov models (CDHMMs). Specifically, we compare two popular frameworks, based on conditional maximum likelihood (CML) and minimum classification error (MCE), to a new framework based on margin maximi ..."
Abstract

Cited by 36 (4 self)
 Add to MetaCart
maximization. Unlike CML and MCE, our formulation of large margin training explicitly penalizes incorrect decodings by an amount proportional to the number of mislabeled hidden states. It also leads to a convex optimization over the parameter space of CDHMMs, thus avoiding the problem of spurious local minima
Distance metric learning for large margin nearest neighbor classification
 In NIPS
, 2006
"... We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
Abstract

Cited by 695 (14 self)
 Add to MetaCart
We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin
Large Margin Training for Hidden Markov Models with Partially Observed States TrinhMinhTri Do
"... Large margin learning of Continuous Density HMMs with a partially labeled dataset has been extensively studied in the speech and handwriting recognition fields. Yet due to the nonconvexity of the optimization problem, previous works usually rely on severe approximations so that it is still an open ..."
Abstract

Cited by 42 (4 self)
 Add to MetaCart
Large margin learning of Continuous Density HMMs with a partially labeled dataset has been extensively studied in the speech and handwriting recognition fields. Yet due to the nonconvexity of the optimization problem, previous works usually rely on severe approximations so that it is still an open
A training algorithm for optimal margin classifiers
 PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
Abstract

Cited by 1865 (43 self)
 Add to MetaCart
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters
Results 1  10
of
27,316