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Bisphenol-A (BPA), an environmental endocrine disruptor, has attracted attention because of its adverse effects on the brain and behavioral development. Previous evidence indicates that perinatal exposure to low levels of BPA affects anxiety-like and cognitive behaviors in adult rodents. The present study aims to investigate the changes of anxiety- and(More)
Our previous study indicated that perinatal exposure to low-dose BPA, one of the most common environmental endocrine disrupters, alters behavioral development in offspring mice. Given that synaptic structure of the hippocampus is closely related to behaviors, in the present study, we examined the effects of perinatal exposure to BPA (0.04, 0.4, and 4.0 mg(More)
Humans are routinely exposed to low levels of bisphenol A (BPA), a synthetic xenoestrogen widely used in the production of polycarbonate plastics. The effects of long-term exposure to BPA on memory and modification of synaptic structure in hippocampus of adult mice were investigated in the present study. The adult mice were exposed to BPA (0.4, 4, and 40(More)
During the last decade, the deluge of multimedia data has impacted a wide range of research areas, including multimedia retrieval, 3D tracking, database management, data mining, machine learning, social media analysis, medical imaging, and so on. Machine learning is largely involved in multimedia applications of building models for classification and(More)
A novel feature selection algorithm is designed for high-dimensional data classification. The relevant features are selected with the least square loss function and $${\ell _{2,1}}$$ ℓ 2 , 1 -norm regularization term if the minimum representation error rate between the features and labels is approached with respect to only these features. Taking into(More)
  • Xingyi Liu
  • 2009 International Joint Conference on Artificial…
  • 2009
Most researches focus on two costs for building cost-sensitive decision trees, such as, misclassification costs, test costs. And the existing literatures always consider the two costs as the same scales, for instance, dollars. However, in real application, it is difficult for us to regard two costs as same scales, for instance, considering misclassification(More)
  • Xingyi Liu
  • 2009 WRI Global Congress on Intelligent Systems
  • 2009
Cost-sensitive learning is popular during the process of classification. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken the trade-off between cost and benefit into account well. In this paper, we present a new strategy for(More)
This paper proposed a novel feature selection method that includes a self-representation loss function, a graph regularization term and an $${l_{2,1}}$$ l 2 , 1 -norm regularization term. Different from traditional least square loss function which focuses on achieving the minimal regression error between the class labels and their corresponding predictions,(More)
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