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Label compression coding strategy aims at multi-label classification problems with high-dimensional and/or sparse label vectors. Without deteriorating classification performance significantly, its efficiency depends on two key aspects: coding raw binary label vectors into real or binary codewords shortly, and decoding binary label vectors from predicted codewords speedily, which reduce the computational...
This paper presents a new neural-network method to describe the electromagnetic (EM) behavior at the interface between the substructures from an internally decomposed EM structure. A set of neural networks is used to represent the EM behavior of the substructure as seen from the interface. This allows EM coupling between substructures to be effectively represented. The method is developed in a finite-element...
For multi-label classification, problem transform algorithms have received more attention due to their good performance and low computational complexity. But how to speed up training and test procedures is still a challenging issue. In this paper, one-by-one data decomposition trick is adopted to divide a k-label problem into k sub-problems, where a specific sub-problem only consists of instances...
Combing one-versus-one decomposition strategy with support vector machine has become an efficient means for multi-label classification problem. But how to speed up its training and test procedures is still a challenging issue. In this paper, we generalize the primary binary support vector machine to construct a double label support vector machine through locating double label instances at marginal...
The prediction of urban water demand using a small number of representative properties is fundamental in evaluating carrying capacity of water resources. Artificial neural networks (ANNs) have recently become popular tools in the prediction of urban water demand. In this paper, an iterative method which combining the strength of back-propagation (BP) in weight learning and genetic algorithmspsila...
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