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View, appearance and pose variations make it difficult to detect human objects only by using linear classification methods. Inspired by the successful applications of L1-norm minimization learning (LML) for human detection, we propose a new nonlinear L1-norm minimization learning method (NL-LML). It integrates a nonlinear transformation with an LML optimization model for human detection. The NL-LML...
This paper proposes a new learning method, which integrates feature selection with classifier construction for human detection via solving three optimization models. Firstly, the method trains a series of weak-classifiers by the proposed L1-norm Minimization Learning (LML) and min-max penalty function models. Secondly, the proposed method selects the weak-classifiers by using the integer optimization...
In recent years, sparse representation originating from signal compressed sensing theory has attracted increasing interest in computer vision research community. However, to our best knowledge, no previous work utilizes L1-norm minimization for human detection. In this paper we develop a novel human detection system based on L1-norm Minimization Learning (LML) method. The method is on the observation...
Latent Dirichlet Allocation (LDA) is a generative model employing the symmetry Dirichlet distribution as prior of the topic-words' distributions to implement model smoothing. When LDA is applied to text classification, smoothing is essential to classification performance. In this paper, we propose a feature-enhanced smoothing method in the idea that words not appeared in the training corpus can help...
The great success of Minimum Phone Error (MPE) training criterion in mono-language large vocabulary continuous speech recognition (LVCSR) tasks motivates us to apply it to bilingual LVCSR systems. In this paper, in conjunction with the previous respectable bilingual phoneme inventory construction techniques, we give a comprehensive investigation to the performance of MPE/fMPE on various Mandarin-English...
In order to alleviate the limitation of "state output probability conditional independence" assumption held by Hidden Markov models (HMMs) in speech recognition, a discriminative semi-parametric trajectory model was proposed in recent years, in which both means and variances in the acoustic models are modeled as time-varying variables. The time- varying information is modeled as a weighted...
The development of speech recognition technology has made it possible for some intelligent query systems to use a voice interface. In this paper, we developed a pop-song music retrieval system for telecom carriers to facilitate the interactions between the end users and the music database. When trying to improve the system performance, however, it was found that some typical recognizing optimization...
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