Outputs (17)

Speeding up parsing of biological context-free grammars (2005)
Book Chapter
Fredouille, D., & Bryant, C. (2005). Speeding up parsing of biological context-free grammars. In A. Apostolico, M. Crochemore, & K. Park (Eds.), Proceedings of the 16th Annual Symposium on Combinatorial pattern matching (241-256). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/11496656_21

Grammars have been shown to be a very useful way to model biological sequences families. As both the quantity of biological sequences and the complexity of the biological grammars increase, generic and efficient methods for parsing are needed. We con... Read More about Speeding up parsing of biological context-free grammars.

Theory completion using inverse entailment (2000)
Book Chapter
Muggleton, S., & Bryant, C. (2000). Theory completion using inverse entailment. In J. Cussens, & A. Frisch (Eds.), Inductive Logic Programming (130-146). London, UK: Springer. https://doi.org/10.1007/3-540-44960-4_8

The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery... Read More about Theory completion using inverse entailment.

Theory completion using inverse entailment (2000)
Book Chapter
Muggleton, S., & Bryant, C. (2000). Theory completion using inverse entailment. In J. Cussens, & A. Frisch (Eds.), Inductive Logic Programming (130-146). London, UK: Springer. https://doi.org/10.1007/3-540-44960-4_8

The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery... Read More about Theory completion using inverse entailment.

Combining active learning with inductive logic programming to close the loop in machine learning (1999)
Book Chapter
programming to close the loop in machine learning. In S. Colton (Ed.), Proceedings of AISB'99 Symposium on AI and Scientific Creativity (59-64). The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB)

Machine Learning (ML) systems that produce human-comprehensible hypotheses from data are typically open loop, with no direct link between the ML system and the collection of data. This paper describes the alternative, it Closed Loop Machine Learning.... Read More about Combining active learning with inductive logic programming to close the loop in machine learning.

Combining active learning with inductive logic programming to close the loop in machine learning (1999)
Book Chapter
programming to close the loop in machine learning. In S. Colton (Ed.), Proceedings of AISB'99 Symposium on AI and Scientific Creativity (59-64). The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB)

Machine Learning (ML) systems that produce human-comprehensible hypotheses from data are typically open loop, with no direct link between the ML system and the collection of data. This paper describes the alternative, it Closed Loop Machine Learning.... Read More about Combining active learning with inductive logic programming to close the loop in machine learning.

Data mining via ILP: The application of progol to a (1997)
Book Chapter
Bryant, C. (1997). Data mining via ILP: The application of progol to a. In N. Lavrac, & S. Dzeroski (Eds.), Inductive logic programming (85-92). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/3540635149_37

As far as this author is aware, this is the first paper to describe the application of Progol to enantioseparations. A scheme is proposed for data mining a relational database of published enantioseparations using Progol. The application of the schem... Read More about Data mining via ILP: The application of progol to a.

Data mining via ILP: The application of progol to a (1997)
Book Chapter
Bryant, C. (1997). Data mining via ILP: The application of progol to a. In N. Lavrac, & S. Dzeroski (Eds.), Inductive logic programming (85-92). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/3540635149_37

As far as this author is aware, this is the first paper to describe the application of Progol to enantioseparations. A scheme is proposed for data mining a relational database of published enantioseparations using Progol. The application of the schem... Read More about Data mining via ILP: The application of progol to a.