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ILP
2007
Springer
10 years 10 months ago
Learning to Assign Degrees of Belief in Relational Domains
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...
Frédéric Koriche
ILP
2007
Springer
10 years 10 months ago
Seeing the Forest Through the Trees
Anneleen Van Assche, Hendrik Blockeel
ILP
2007
Springer
10 years 10 months ago
Bias/Variance Analysis for Relational Domains
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...
Jennifer Neville, David Jensen
ILP
2007
Springer
10 years 10 months ago
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates
Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). W...
Mark Goadrich, Jude W. Shavlik
ILP
2007
Springer
10 years 10 months ago
Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming
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...
Louis Oliphant, Jude W. Shavlik
ILP
2007
Springer
10 years 10 months ago
Using ILP to Construct Features for Information Extraction from Semi-structured Text
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,...
Ganesh Ramakrishnan, Sachindra Joshi, Sreeram Bala...
ILP
2007
Springer
10 years 10 months ago
Building Relational World Models for Reinforcement Learning
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...
Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richa...
ILP
2007
Springer
10 years 10 months ago
Structural Statistical Software Testing with Active Learning in a Graph
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...
Nicolas Baskiotis, Michèle Sebag
ILP
2007
Springer
10 years 10 months ago
Learning with Kernels and Logical Representations
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...
Paolo Frasconi
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