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We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role(More)
Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different(More)
—Discriminative language modeling (DLM) is a feature based approach that is used as an error-correcting step after hypothesis generation in automatic speech recognition (ASR). We formulate this both as a classification and a ranking problem and employ the perceptron, the margin infused relaxed algorithm (MIRA) and the support vector machine (SVM). To(More)
This paper investigates various approaches to data sampling and dimensionality reduction for discriminative language models (DLM). Being a feature based language modeling approach, the aim of DLM is to rerank the ASR output with discriminatively trained feature parameters. Using a Turkish morphology based feature set, we examine the use of online Principal(More)
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