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Data driven frequency mapping for kernels used in support vector machines
Data driven frequency mapping for kernels used in support vector machines
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机译:支持向量机中使用的内核的数据驱动频率映射
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摘要
Frequency features to be used for binary classification of data using a linear classifier are selected by determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data. A mapping function is constructed for each hypothesis. The mapping functions are applied to the training data to generate frequency features, and a subset of the frequency are selecting iteratively. The linear function is then trained using the subset of frequency features and labels of the training data.
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