C. H. Bryant and S. H. Muggleton. Closed loop machine learning. Tech- nical Report YCS 330, University of York, Department of Computer Science, Heslington, York, YO10 5DD, UK., 2000. Home/Search Document Details and Download
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Machine Learning and Inductive Logic Programming for.. - Kazakov, Kudenko (2001) (Correct)
....Active learning [40] extends a standard supervised learner requiring expensive annotated data with a module browsing a much larger unannotated dataset and selecting for annotation the examples that are considered the most beneficial to the learning process. Closed Loop Machine Learning (CLML) [4] couples ML with a robot carrying out experiments. In this way the learning algorithm can suggest a hypothesis and verify it experimentally if the hypothesis is rejected, the collected data can give rise to a new round of the same cycle. 3 Machine Learning for (Single) Agents Most of the ....
C. H. Bryant and S. H. Muggleton. Closed loop machine learning. Technical Report YCS 330, University of York, Department of Computer Science, Heslington, York, YO10 5DD, UK., 2000.
Closed Loop Machine Learning: Complexity Of Ase-Progol - Tamaddoni-Nezhad, Muggleton (2002) Self-citation (Muggleton) (Correct)
....system which uses the Inductive Logic Programming (ILP) system Progo15. 0 [7] for generating hypotheses together with a CART like algorithm [2] to select trials which minimise the expected cost of experimentation. More details about the design and implementation of ASE Progol can be found in [3, 4]. To date, ASE Progol has been tested on: a) a small and simplified model of functional genomics and b) a metabolic pathway from the aromatic amino acid pathway of yeast. The results of these studies which correspond to phase A and phase B of the project are reported in [3] and [4] respectively. ....
....can be found in [3, 4] To date, ASE Progol has been tested on: a) a small and simplified model of functional genomics and b) a metabolic pathway from the aromatic amino acid pathway of yeast. The results of these studies which correspond to phase A and phase B of the project are reported in [3] and [4] respectively. In this report we study the complexity of each implemented component of ASE Progol. In section 2 we review each component of ASE Progol and discuss the complexity of each component. In the second part of the report, we perform an experimentation to compare the average run ....
[Article contains additional citation context not shown here]
C. H. Bryant and S. H. Muggleton. Closed loop machine learning. Tech- nical Report YCS 330, University of York, Department of Computer Science, Heslington, York, YO10 5DD, UK., 2000.
Closed Loop Machine Learning: Reproduction and.. - Tamaddoni-Nezhad.. (2001) Self-citation (Muggleton) (Correct)
....system which uses the Inductive Logic Programming (ILP) system Progo15. 0 [6] for generating hypotheses together with a CART like algorithm [2] to select trials which minimize the expected cost of experimentation. More details about the design and implementation of ASE Progol can be found in [3, 4]. To date, ASE Progol has been tested on: a) a small and simplified model of functional genomics and b) a metabolic pathway from the aromatic amino acid pathway of yeast. The results of these studies which correspond to phase A and phase B of the project are reported in [3] and [4] respectively. ....
....can be found in [3, 4] To date, ASE Progol has been tested on: a) a small and simplified model of functional genomics and b) a metabolic pathway from the aromatic amino acid pathway of yeast. The results of these studies which correspond to phase A and phase B of the project are reported in [3] and [4] respectively. In this report we have reproduced the results of phase A experiments using the same experimental method as used in [3] We have also used a test strategy for measuring the predictive accuracy of ASE Progol which is different from the test strategy used in the previous ....
[Article contains additional citation context not shown here]
C. H. Bryant and S. H. Muggleton. Closed loop machine learning. Tech- nical Report YCS 330, University of York, Department of Computer Science, Heslington, York, YO10 5DD, UK., 2000.
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