Incorporating prior domain knowledge into inductive machine learning : its implementation in contemporary capital markets
- Publication Type:
- Thesis
- Issue Date:
- 2007
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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.
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