ILP
2007
Springer
14 years 6 months ago
2007
Springer
A recurrent question in the design of intelligent agents is how to assign degrees of beliefs, or subjective probabilities, to various events in a relational environment. In the sta...
ILP
2007
Springer
14 years 6 months ago
2007
Springer
ILP
2007
Springer
14 years 6 months ago
2007
Springer
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
ILP
2007
Springer
14 years 6 months ago
2007
Springer
Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). W...
ILP
2007
Springer
14 years 6 months ago
2007
Springer
ILP
2007
Springer
14 years 6 months ago
2007
Springer
Stochastically searching the space of candidate clauses is an appealing way to scale up ILP to large datasets. We address an approach that uses a Bayesian network model to adaptive...
ILP
2007
Springer
14 years 6 months ago
2007
Springer
Machine-generated documents containing semi-structured text are rapidly forming the bulk of data being stored in an organisation. Given a feature-based representation of such data,...
ILP
2007
Springer
14 years 6 months ago
2007
Springer
Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
ILP
2007
Springer
14 years 6 months ago
2007
Springer
Structural Statistical Software Testing (SSST) exploits the control flow graph of the program being tested to construct test cases. Specifically, SSST exploits the feasible paths...
ILP
2007
Springer
14 years 6 months ago
2007
Springer
In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. The relational representation of data and background...