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Combining active learning with inductive logic programming to close the loop in machine learning.
| Content Provider | CiteSeerX |
|---|---|
| Author | C. H. Bryant S., H. Muggleton C., D. |
| Abstract | 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, Closed Loop Machine Learning. This is related to the area of Active Learning in which the ML system actively selects experiments to discriminate between contending hypotheses. In Closed Loop Machine Learning the system not only selects but also carries out the experiments in the learning domain. ASE-Progol, a Closed Loop Machine Learning system, is proposed. ASE-Progol will use the ILP system Progol to form the initial hypothesis set. It will then devise experiments to select between competing hypotheses, direct a robot to perform the experiments, and finally analyse the experimental results. ASE-Progol will then revise its hypotheses and repeat the cycle until a unique hypothesis remains. This will be, to our knowledge, the first attempt to use a robot to carry out experiments selected by Active Learning within a real world application. 1 Knowledge discovery in biological and chemical domains The pharmaceutical industry is increasingly overwhelmed by large-volume-data. This is generated both internally as |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Active Learning Machine Learning Inductive Logic Programming Ml System First Attempt Closed Loop Machine Learning Chemical Domain Direct Link Initial Hypothesis Set Learning Domain Ilp System Progol Knowledge Discovery Closed Loop Machine Learning System Loop Machine Learning Pharmaceutical Industry Unique Hypothesis Open Loop Real World Application Experimental Result Human-comprehensible Hypothesis |
| Content Type | Text |
| Resource Type | Article |
