Te-Kang Jan

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Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the(More)
In this paper, a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network (MPNN). The proposed model is shown to provide improved regularization with reduced computation utilizing semiparametric model approach and efficient vector quantization of data space. In this paper, the proposed model is shown(More)
We propose a pathogen-classification system using the Surface-Enhanced Raman Scattering (SERS) platform. The system differentiates the pathogens based on their SERS spectra, which are believed to be related to the surface chemical components. The specialty of the system is to not only consider the usual classification accuracy, but also pay attention to the(More)
Learning to rank has become a popular research topic in several areas such as information retrieval and machine learning. Pair-wise ranking, which learns all the order preferences between pairs of examples, is a typical method for solving the ranking problem. In pair-wise ranking, Rank SVM is a widely-used algorithm and has been successfully applied to the(More)
Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the(More)
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