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k-nearest neighbors (k-NN) voting rules are an effective tool in countless many machine learning techniques. In spite of its simplicity, k-NN classification is very attractive to practitioners, as it has shown very good performances in practical applications. However, it suffers from various drawbacks, like sensitivity to “noisy” prototypes and poor generalization properties when dealing with sparse,...
An assessment method for water shortage risk based on neural network classificatory of fuzzy sets is presented in paper. Risk rate, weakness, possibility of recovery, period for reoccurrence and risk level are defined as the indexes for water shortage risk assessment of regional resources. The suggested model is used to evaluate water shortage risk of Zhanghe irrigation region in Hubei Province in...
Handling large amounts of data, such as large image databases, requires the use of approximate nearest neighbor search techniques. Recently, Hamming embedding methods such as spectral hashing have addressed the problem of obtaining compact binary codes optimizing the trade-off between the memory usage and the probability of retrieving the true nearest neighbors. In this paper, we formulate the problem...
Prostate cancer is a disease which is the most common and which is also the second deadly in men. When prostate cancer can be diagnosed early, medical surgery operation can be performed and the disease can be treated. In this study, the aim is to design a classifier based expert system for early diagnosis of the organ in constraint phase. The other purpose is to reach informed decision making without...
In this paper, we have established one credit risk evaluation model based on learning vector quantization respectively. This model is used to identify two patterns samples of Chinese listed companies, including training samples of 285 listed companies (59 companies with special treatment and 226 normal companies) and test samples of 117 listed companies(29 companies with special treatment and 88 normal...
Neural network is a widely used and an effective artificial intelligence technique used for predictions and classifications which has been developed based on human biological neural system. Determining the structure of a neural network is a very complex task and there is no defined approach to determine the structure, especially the number of hidden nodes. Traditionally the number of hidden nodes...
To meet the robustness of the fault diagnosis algorithm for identifying the novel fault pattern, the method, which combines the supervised classification and unsupervised classification, is proposed in this paper. As the supervised classification, Learning vector quantity neural network is employed to classify sensor mode. As the unsupervised classification, subtractive clustering is applied to identify...
This paper presents a novel rough-based feature selection method for gene expression data analysis. It can find the relevant features without requiring the number of clusters to be known a priori and identify the centers that approximate to the correct ones. In this paper, we attempt to introduce a prediction scheme that combines the rough-based feature selection method with radial basis function...
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