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Typical training criteria for string recognition like for example minimum phone error (MPE) and maximum mutual information (MMI) in speech recognition are based on a (regularized) loss function. In contrast, large-margin classifiers-the de-facto standard in machine learning-maximize the separation margin. An additional loss term penalizes misclassified samples. This paper shows how typical training...
We present a new technique that employs support vector machines and Gaussian mixture densities to create a generative/discriminative joint classifier. In the past, several approaches to fuse the advantages of generative and discriminative approaches were presented, often leading to improved robustness and recognition accuracy. The presented method directly fuses both approaches, effectively allowing...
We present a new method for relevance feedback in image retrieval and a scheme to learn weighted distances which can be used in combination with different relevance feedback methods. User feedback is a crucial step in image retrieval to maximise retrieval performance as was shown in recent image retrieval evaluations. Machine learning is expected to be able to learn how to rank images according to...
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