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This paper introduces a novel tree induction algorithm called sequential Random Forest (sRF) to improve the detection accuracy of a standard Random Forest classifier. Observations have shown that the overall performance of a forest is strongly influenced by the number of training samples. The main idea is to sequentially adapt the number of training samples per class so that each tree better complements...
The directionality of protein interactions is the prerequisite of forming various signaling networks, and the construction of signaling networks is a critical issue in the discovering the mechanism of the life process. In this paper, we proposed a novel method to infer the directionality in protein-protein interaction networks and furthermore construct signaling networks. Based on the functional annotations...
Natural scene recognition and classification have received considerable attention in the computer vision community due to its challenging nature. Significant intra-class variations have largely limited the accuracy of scene categorization tasks: a holistic representation forces matching in strict spatial confinement; whereas a bag of features representation ignores the order or spatial layout of the...
The proliferation of deep Web offers users a great opportunity to search high-quality information from Web. As a necessary step in deep Web data integration, the goal of duplicate entity identification is to discover the duplicate records from the integrated Web databases for further applications(e.g. price-comparison services). However, most of existing works address this issue only between two data...
In this paper, the establishment of the neural network model of forecasting short-term power load in an electric power grid is studied. Basing on the model, the BP algorithm for power load is explored. The research on BP network model includes determining the hidden layer number, hidden layer nodes number, training frequency and accuracy of learning rate. In this paper, we focus on that how to give...
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