| 摘 要： | It is known that latent semantic indexing （LSI） takes advantage of implicit higher-order （or latent） structure in the association of terms and documents. Higher-order relations in LSI capture ＂latent semantics＂. These findings have inspired a novel Bayesian framework for classification named Higher-Order Naive Bayes （HONB）, which was introduced previously, that can explicitly make use of these higher-order relations. In this paper, we present a novel semantic smoothing method named Higher-Order Smoothing （HOS） for the Naive Bayes algorithm. HOS is built on a similar graph based data representation of the HONB which allows semantics in higher-order paths to be exploited. We take the concept one step further in HOS and exploit the relationships between instances of different classes. As a result, we move beyond not only instance boundaries, but also class boundaries to exploit the latent information in higher-order paths. This approach improves the parameter estimation when dealing with insufficient labeled data. Results of our extensive experiments demonstrate the value of HOS oi1 several benchmark datasets.
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