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The support vector machine is a powerful supervised learning algorithm that has been successfully applied to a plenty of fields including text and image recognition, medical diagnosis and so on. The kernel and its parameters optimization, formally known as model selection, is a crucial factor which influences a good tradeoff between bias and variance. To automate model selection of support vector...
To improve the learning and generalization ability of the machine-learning model, a new compound kernel that may pay attention to the similar degree between sample space and feature space is proposed. In this paper, used the new compound kernel support vector machine to a speech recognition system for Chinese isolated words, non-specific person and middle glossary quantity, and compared the speech...
Isomap is one of widely-used low-dimensional embedding methods. However, in many scenarios, the data come sequentially and the effect of the data is accumulated. Isomap algorithms have no the ability of new data be added for all data need to be available when estimates the geodesic distances. In this paper we propose an incremental learning isomap algorithm, which take the approximate geodesic distance...
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