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.

Bibliography: leaves 156-161. / v, 161 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Economics, 2000?

Dissertation--Universität Hamburg, 2006. / Includes bibliographical references.

Thesis (Cand. Polit degree)--Institute for Sociology and Human Geography, 2001. / Includes bibliographical references (p. 80-84).

Thesis (LL. M.)--University of Toronto, 2004. / Adviser: Doug Harris.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available in print.

Thesis (M. Ec.) -- University of Adelaide, Faculty of Economics, 1975.

Thesis (Ph.D.) -- University of Adelaide, Dept. of Economics, 2000? / Bibliography: leaves 156-161.

Thesis (Th. M.)--Grace Theological Seminary, 1970. / Includes bibliographical references (leaves 100-105).

Thesis (Ph. D.)--University of Sydney, 2002. / Title from title screen (viewed 15 April 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Dept. of Economics. Degree awarded 2002; thesis submitted 2001. Includes bibliographical references. Also available in print form.