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您的检索式：作者名="Raymond Chiong"
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| 1 | Remote heart rate measurement using low-cost RGB face video: a technical literature review显示文摘 | Philipp V. ROUAST Marc T. P. ADAM Raymond CHIONG David CORNFORTH Ewa LUX | 2018 | Frontiers of Computer Science2018，12，5： | 12 |
| 2 | Evolutionary Optimization: Pitfalls and Booby Traps显示文摘Evolutionary computation (EC), a collective name for a range of metaheuristic black-box optimization algorithms, is one of the fastest-growing areas in computer science. Many manuals and 'how-to's on the use of different EC methods as well as a variety of free or commercial software libraries are widely available nowadays. However, when one of these methods is applied to a real-world task, there can be many pitfalls and booby traps lurking - certain aspects of the optimization problem that may lead to unsatisfactory results even if the algorithm appears to be correctly implemented and executed. These include the convergence issues, ruggedness, deceptiveness, and neutrality in the fitness landscape, epistasis, non-separability, noise leading to the need for robustness, as well as dimensionality and scalability issues, among others. In this article, we systematically discuss these related hindrances and present some possible remedies. The goal is to equip practitioners and researchers alike with a clear picture and understanding of what kind of problems can render EC applications unsuccessful and how to avoid them from the start. | Thomas Weise Raymond Chiong Ke Tang | 2012 | Journal of Computer Science & Technology2012，27，5： | 5 |
| 3 | Profit Guided or Statistical Error Guided? A Study of Stock Index Forecasting Using Support Vector Regression显示文摘Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨?ve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies thatprofit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices. | HU Zhongyi BAO Yukun CHIONG Raymond XIONG Tao | 2017 | Journal of Systems Science & Complexity2017，30，6： | 1 |
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