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Forecasting the daily direction of the stock market has long been a fascinating problem. Many methods with different features have been proposed with varying degrees of accuracy. We apply support vector machines (SVM) and gradient boosting (GB) to this problem, focusing on the NASDAQ index. Some special features are used, including features extracted from the global markets. We compare the performance...
Feature selection has been an active research area in the past decades. The objective of feature selection includes improving prediction accuracy, accelerating classification speed, and gaining better understanding of the features. Feature selection methods are often divided into three categories: filter methods, wrapper methods, and embedded methods. In this paper, we propose a simple leave-one-feature-out...
The problem of computing the minimum distance between two convex hulls has applications to many areas including robotics, computer graphics, path planning, and data classification. In this paper, we propose and investigate a new algorithm (based on the MDM algorithm) for finding the minimum distance between two convex hulls. The new algorithm is simple to understand and easy to implement. The convergence...
Geometric methods provide an intuitive and theoretically solid viewpoint for the solution of many optimization problems in the fields of pattern recognition and machine learning. The support vector machine (SVM) classification is a typical optimization task that has achieved excellent generalization performance in a wide variety of applications. In this paper, the notion of “scaled convex hull” (SCH)...
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