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Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best...
Stock market predictions comprise challenging applications of modern time series forecasting and are essential to the success of many businesses and financial institutions. In this paper, a novel nonlinear combination model is presented for stock market forecasting, which based on Support Vector Machine (SVM) regression combining the linear regression of traditional statistical model with the nonlinear...
Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw data from Taiwan...
The paper compares the classification performance rate of eight models: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), case-base reasoning (CBR), and three decision trees (DTs). We build models and test their classification accuracy rates on a historical data set provided by a German financial institution. The data set contains...
In this paper, we use support vector machine (SVM) and artificial neural networks to diagnosis hepatitis diseases. Furthermore, we use those networks to identify the type and the phase of disease. Considering the most important hepatitis cases leads us to six classes: hepatitis B (two phases), hepatitis C (two phases), non-viral hepatitis and no-hepatitis. For this purpose, we design various networks...
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