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Yield is a very important criterion to measure the semiconductor wafer fabrication facilities (FABs) productivity. The finished products will be check by Wafer Acceptance Test (WAT) and Circuit Probe (CP) to classified into ferior goods or inferior goods. This research applied the data from WAT and CP for the selection of the most important measuring parameters to improve the yield. Three methods, namly Support Vector Regression (SVR), Group Method of Data Handling (GMDH), Genetic Algorithm-Backpropagation Neural Network (GA-BPNN), were applied to model the system and were compared to investigate the best variable combination among 164 variables. It was found that the data need to be first classified in order to enhance the performances. Also, GA-BPNN out performed other methods using only 9 variables. The results were confirmed by engineers and used in FABs to improve the yield by controlling these parameters.

The volatility of real estate prices is one of the key factors to the decision making of financial institutions, as well as to a country's economic development. Therefore how to catch the trend of this real estate price has been an important issue for Governments and financial institutions. In this study, we discuss the trend of average unit price in a capital city, in hope of establishing a decent predicting model and key factors for this price. Other than traditional statistic methods, Neural Networks (NN) and Support Vector Regression (SVR) have demonstrated their advantages in previous research, and thus are applied and compared in this study. Variables are first summarized and concluded from earlier research and then selected by stepwise procedure. The result shows that SVR outperformed NN and stepwise procedure is valid in variable selections, and the key factors are previous trading price,