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

, 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

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

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.

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

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

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

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

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 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