Incorporating prior domain knowledge into inductive machine learning : its implementation in contemporary capital markets

Publication Type:
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail01front.pdf58.14 kB
Adobe PDF
Thumbnail02whole.pdf1.67 MB
Adobe PDF
An ideal inductive machine learning algorithm produces a model best approximating an underlying target function by using reasonable computational cost. This requires the resultant model to be consistent with the training data, and generalize well over the unseen data. Regular inductive machine learning algorithms rely heavily on numerical data as well as general-purpose inductive bias. However certain environments contain rich domain knowledge prior to the learning task, but it is not easy for regular inductive learning algorithms to utilize prior domain knowledge. This thesis discusses and analyzes various methods of incorporating prior domain knowledge into inductive machine learning through three key issues: consistency, generalization and convergence. Additionally three new methods are proposed and tested over data sets collected from capital markets. These methods utilize financial knowledge collected from various sources, such as experts and research papers, to facilitate the learning process of kernel methods (emerging inductive learning algorithms). The test results are encouraging and demonstrate that prior domain knowledge is valuable to inductive learning machines.
Please use this identifier to cite or link to this item: