Jen-Yuan Yeh∗
Computer Systems Science and Engineering, Vol.33, No.1, pp. 41-52, 2018, DOI:10.32604/csse.2018.33.041
Abstract As a crucial task in information retrieval, ranking defines the preferential order among the retrieved documents for a given query. Supervised learning
has recently been dedicated to automatically learning ranking models by incorporating various models into one effective model. This paper proposes a
novel supervised learning method, in which instances are represented as bags of contexts of features, instead of bags of features. The method applies
rank-order correlations to measure the correlation relationships between features. The feature vectors of instances, i.e., the 1st-order raw feature vectors,
are then mapped into the feature correlation space via More >
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