LARGE MARGIN TRAINING OF ACOUSTIC MODELS FOR SPEECH RECOGNITION Fei Sha Advisor: Prof. Lawrence K. Saul Automatic speech recognition (ASR) depends critically on building acoustic models for linguistic units. These acoustic models usually take the form of continuous-density hidden Markov models (CD-HMMs), whose parameters are obtained by maximum likelihood estimation. Recently, however, there ha...

price transmission is one of the most important factors that affects the producers, consumers and marketing agents’ welfare. market structure affects price transmission. asymmetric price transmission affects the marketing margin, increases marketing agents’ profit and decreases producers and consumers’ welfare. in recent years high prices of beef has caused consumers’ dissatisfaction however, p...

In this paper, we propose a novel large margin classifier, called the maxi-min margin machine M(4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only locally...

Perceptron like large margin algorithms are introduced for the experiments with various margin selections. Compared to the previous perceptron reranking algorithms, the new algorithms use full pairwise samples and allow us to search for margins in a larger space. Our experimental results on the data set of (Collins, 2000) show that a perceptron like ordinal regression algorithm with uneven marg...

Until now, approaches to web content extraction have focused on random field models, largely neglecting large margin methods. Structured large margin methods, however, have recently shown great practical success. We compare, for the first time, greedy and structured support vector machines with conditional random fields on a real-world web news content extraction task, showing that large margin...

The large-margin technique has served as the foundation of several successful theoretical and empirical results in multi-label image classification. However, most techniques are only suitable to shallow models with preset feature representations a few neural networks enforce margins at output layer, which not well for deep networks. Based on technique, rank loss function any network structure i...