Information-based objective functions for active data selection
Neural Computation
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Scouting Context-Sensitive Components
EH '01 Proceedings of the The 3rd NASA/DoD Workshop on Evolvable Hardware
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
Active learning with statistical models
Journal of Artificial Intelligence Research
Active learning for regression based on query by committee
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Characterising enzymes for information processing: microfluidics for autonomous experimentation
UC'10 Proceedings of the 9th international conference on Unconventional computation
Characterising enzymes for information processing: microfluidics for autonomous experimentation
UC'10 Proceedings of the 9th international conference on Unconventional computation
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The information processing capabilities of many proteins are currently unexplored. The complexities and high dimensional parameter spaces make their investigation impractical. Difficulties arise as limited resources prevent intensive experimentation to identify repeatable behaviours. To assist in this exploration, computational techniques can be applied to efficiently search the space and automatically generate probable response behaviours. Here an artificial experimenter is discussed that aims to mimic the abilities of a successful human experimenter, using multiple hypotheses to cope with the small number of observations practicable. Coupling this approach with a lab-on-chip platform currently in development, we seek to create an autonomous experimentation machine capable of enzyme characterisation, which can be used as a tool for developing enzymatic computing.