Lazy Transformation-Based Learning
We introduce a significant improvement for a relatively new machine learning method called Transformation-Based Learning. By applying a Monte Carlo strategy to randomly sample from the space of rules, rather than exhaustively analyzing all possible rules, we drastically reduce the memory and time costs of the algorithm, without compromising accuracy on unseen data. This enables Transformation-Based Learning to apply to a wider range of domains, as it can effectively consider a larger number of different features and feature interactions in the data. In addition, the Monte Carlo improvement decreases the labor demands on the human developer, who no longer needs to develop a minimal set of rule templates to maintain tractability.
This paper,applies the LSTAR model to an empirical study of the Shanghai stock A index.The results show that there is significant characteristic of nonlinear logistic transition in the Shanghai stock A market.The LSTAR model successfully detects the cyclical behavior and makes improvement in forecast performance relative to a linear AR model.