Machine Learning
Learning Domain Theories using Abstract Beckground Knowledge
ECML '93 Proceedings of the European Conference on Machine Learning
ILPS '97 International Seminar on Logic Databases and the Meaning of Change, Transactions and Change in Logic Databases
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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Both intensional and extensional background knowledge have previously been used in inductive problems to complement the training set used for a task In this research, we propose to explore the usefulness, for inductive learning, of a new kind of intensional background knowledge: the inter-relationships or conditional probability distributions between subsets of attributes Such information could be mined from publicly available knowledge sources but including only some of the attributes involved in the inductive task at hand The purpose of our work is to show how this information can be useful in inductive tasks, and under what circumstances We will consider injection of background knowledge into Bayesian Networks and explore its effectiveness on training sets of different sizes We show that this additional knowledge not only improves the estimate of classification accuracy, it also reduces the variance in the accuracy of the model.