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A discriminatively trained, multiscale, deformable part model

by Pedro Felzenszwalb, David Mcallester, Deva Ramanan - In IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2008 , 2008
"... This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge ..."
Abstract - Cited by 555 (11 self) - Add to MetaCart
This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007

Object Detection with Discriminatively Trained Part Based Models

by Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
Abstract - Cited by 1422 (49 self) - Add to MetaCart
, their value had not been demonstrated on difficult benchmarks such as the PASCAL datasets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM

Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms

by Michael Collins , 2002
"... We describe new algorithms for training tagging models, as an alternative to maximum-entropy 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 ..."
Abstract - Cited by 660 (13 self) - Add to MetaCart
We describe new algorithms for training tagging models, as an alternative to maximum-entropy 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

Discriminative Training and Maximum Entropy Models for Statistical Machine Translation

by Franz Josef Och, Hermann Ney , 2002
"... We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source -channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language senten ..."
Abstract - Cited by 508 (30 self) - Add to MetaCart
We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source -channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible hidden variables.

Discriminative Training on language model

by Zheng Chen, Kai-Fu Lee, Ming-ting Li
"... a lot of problems, including speech recognition, handwriting, Chinese pinyin-input etc. In recognition, statistical language model, such as trigram, is used to provide adequate information to predict the probabilities of hypothesized word sequences. The traditional method relying on distribution est ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
estimation are sub-optimal when the assumed distribution form is not the true one, and that "optimality" in distribution estimation does not automatically translate into "optimality" in classifier design. This paper proposed a discriminative training method to minimize the error rate

P.C.: Minimum phone error and I-smoothing for improved discriminative training

by D. Povey, P. C. Woodl - In: Proc. ICASSP , 2002
"... In this paper we introduce the Minimum Phone Error (MPE) and Minimum Word Error (MWE) criteria for the discriminative train-ing of HMM systems. The MPE/MWE criteria are smoothed ap-proximations to the phone or word error rate respectively. We also discuss I-smoothing which is a novel technique for s ..."
Abstract - Cited by 250 (13 self) - Add to MetaCart
In this paper we introduce the Minimum Phone Error (MPE) and Minimum Word Error (MWE) criteria for the discriminative train-ing of HMM systems. The MPE/MWE criteria are smoothed ap-proximations to the phone or word error rate respectively. We also discuss I-smoothing which is a novel technique

The Influence of Discrimination Training on Pronunciation

by Theodore H. Niedzielski, Theodore H. Mueller, Henri Niedzielski
"... This study conducted at the University of Kentucky in 1967-68 tests the theory of interaction between discrimination and pronunciation through a field test. After a brief review of previous research in phonemic discrimination as related to foreign language learnin.g, the procedures and results of th ..."
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This study conducted at the University of Kentucky in 1967-68 tests the theory of interaction between discrimination and pronunciation through a field test. After a brief review of previous research in phonemic discrimination as related to foreign language learnin.g, the procedures and results

An overview of discriminative training for speech recognition

by Keith Vertanen
"... This paper gives an overview of discriminative training as it pertains to the speech recognition problem. The basic theory of discriminative training will be discussed and an explanation of maximum mutual information (MMI) given. Common problems inherent to discriminative training will be explored a ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
This paper gives an overview of discriminative training as it pertains to the speech recognition problem. The basic theory of discriminative training will be discussed and an explanation of maximum mutual information (MMI) given. Common problems inherent to discriminative training will be explored

Hope and fear for discriminative training of

by David Chiang, Michael Collins
"... In machine translation, discriminative models have almost entirely supplanted the classical noisychannel model, but are standardly trained using a method that is reliable only in low-dimensional spaces. Two strands of research have tried to adapt more scalable discriminative training methods to mach ..."
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In machine translation, discriminative models have almost entirely supplanted the classical noisychannel model, but are standardly trained using a method that is reliable only in low-dimensional spaces. Two strands of research have tried to adapt more scalable discriminative training methods

Discriminative Training For Continuous Speech Recognition

by Reichl And Ruske, W. Reichl, G. Ruske - Proc. 1995 Europ. Conf. on Speech Communication and Technology , 1995
"... Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully applied for automatic speech recognition. In this paper a discussion of the Minimum Classification Error and the Maximum Mutual Information objective is presented. An extended reestimation formula is ..."
Abstract - Cited by 17 (0 self) - Add to MetaCart
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully applied for automatic speech recognition. In this paper a discussion of the Minimum Classification Error and the Maximum Mutual Information objective is presented. An extended reestimation formula
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