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All Outputs (33)

Learning Chomsky-like grammars for biological sequence families (2000)
Conference Proceeding
Muggleton, S., Bryant, C., & Srinivasan, A. (2000). Learning Chomsky-like grammars for biological sequence families. In P. Langley (Ed.), Proceedings of the 17th International Conference on Machine Learning (631-638)

This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The... Read More about Learning Chomsky-like grammars for biological sequence families.

Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study (2000)
Conference Proceeding
Muggleton, S., Bryant, C., & Srinivasan, A. (2000). Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study. In R. de Mántaras, & E. Plaza (Eds.), Machine learning: ECML 2000: 11th European conference on machine learning, Barcelona, Catalonia, Spain, May 31-June 2 2000 (300-312)

This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The... Read More about Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study.

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.

Knowledge discovery in databases: application to chromatography (1998)
Journal Article
Bryant, C., & Rowe, R. (1998). Knowledge discovery in databases: application to chromatography. Trends in Analytical Chemistry, 17(1), 18-24. https://doi.org/10.1016/S0165-9936%2897%2900094-0

This paper reviews emerging computer techniques for discovering knowledge from databases and their application to various sets of separation data. The data-sets include the separation of a diverse range of analytes using either liquid, gas or ion chr... Read More about Knowledge discovery in databases: application to chromatography.

Using inductive logic programming to discover knowledge hidden in chemical data (1998)
Journal Article
Bryant, C., Adam, A., Taylor, D., & Rowe, R. (1998). Using inductive logic programming to discover knowledge hidden in chemical data. Chemometrics and Intelligent Laboratory Systems, 36(2), 111-123. https://doi.org/10.1016/S0169-7439%2897%2900023-3

This paper demonstrates how general purpose tools from the field of Inductive Logic Programming (ILP) can be applied to analytical chemistry. As far as these authors are aware, this is the first published work to describe the application of the ILP t... Read More about Using inductive logic programming to discover knowledge hidden in chemical data.

Transforming general program proofs: a meta interpreter which expands negative literals (1997)
Presentation / Conference
West, M., Bryant, C., & McCluskey, T. (1997, July). Transforming general program proofs: a meta interpreter which expands negative literals. Presented at 7th International Workshop on Logic Program Synthesis and Transformation, Leuven, Belgium

This paper provides a method for generating a proof tree from an instance and a general logic program viz one which includes negative literals. The method differs from previous work in the field in that negative literals are first unfolded and then... Read More about Transforming general program proofs: a meta interpreter which expands negative literals.

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.

The validation of formal specifications of requirements (1996)
Conference Proceeding
McCluskey, T., Porteous, J., Bryant, C., & West, M. (1996). The validation of formal specifications of requirements. . https://doi.org/10.14236/ewic/FA1996.14

We review the approaches put forward to validate formal specifications of requirements, drawing a parallel with research into the validation of knowledge bases. Using an industrial-scale case study we describe a partially implemented, integrated envi... Read More about The validation of formal specifications of requirements.

Towards an expert system for enantioseparations: induction of rules using machine learning (1996)
Journal Article
Bryant, C., Adam, A., Taylor, D., & Rowe, R. (1996). Towards an expert system for enantioseparations: induction of rules using machine learning. Chemometrics and Intelligent Laboratory Systems, 34(1), 21-40. https://doi.org/10.1016/0169-7439%2896%2900016-0

A commercially available machine induction tool was used in an attempt to automate the acquisition of the knowledge needed for an expert system for enantioseparations by High Performance Liquid Chromatography using Pirkle-type chiral stationary phase... Read More about Towards an expert system for enantioseparations: induction of rules using machine learning.

Discovering knowledge hidden in a chemical database using a commercially available data mining tool (1995)
Presentation / Conference
Bryant, C., Adam, A., Taylor, D., Conroy, G., & Rowe, R. (1995, February). Discovering knowledge hidden in a chemical database using a commercially available data mining tool. Presented at IEE Colloquium on Knowledge Discovery in Databases, London, UK

Describes DataMariner, a commercially available tool that is designed to facilitate the discovery of knowledge hidden in databases. The potential of the tool for scientific applications is illustrated via a case study. This is both the first applicat... Read More about Discovering knowledge hidden in a chemical database using a commercially available data mining tool.

A review of expert systems for chromatography (1994)
Journal Article
Bryant, C., Adam, A., Taylor, D., & Rowe, R. (1994). A review of expert systems for chromatography. Analytica Chimica Acta, 297(3), 317-347. https://doi.org/10.1016/0003-2670%2894%2900209-6

Expert systems for chromatography are reviewed. A taxonomy is proposed that allows present (and future) expert systems in this area to be classified and facilitates an understanding of their inter-relationship. All the systems are described focusing... Read More about A review of expert systems for chromatography.