Observation Tree Approach: Active Learning Relying on Testing
نویسندگان
چکیده
منابع مشابه
Learning from Faults: Mutation Testing in Active Automata Learning - Mutation Testing in Active Automata Learning
System verification is often hindered by the absence of formal models. Peled et al. proposed black-box checking as a solution to this problem. This technique applies active automata learning to infer models of systems with unknown internal structure. This kind of learning relies on conformance testing to determine whether a learned model actually represents the considered system. Since conforma...
متن کاملDecision Tree Instability and Active Learning
Decision tree learning algorithms produce accurate models that can be interpreted by domain experts. However, these algorithms are known to be unstable – they can produce drastically different hypotheses from training sets that differ just slightly. This instability undermines the objective of extracting knowledge from the trees. In this paper, we study the instability of the C4.5 decision tree...
متن کاملActive Batch Learning with Tree Ensembles
In a conventional machine learning approach, one uses labeled data to train the model. However, often we have a data set with few labeled instances, and a large number of unlabeled ones. This is called a semi-supervised learning problem. It is well known that often unlabeled data could be used to improve a model. In real world scenarios, labeled data can usually be obtained dynamically. However...
متن کاملActive Perspectives on Computational Learning and Testing Thesis Proposal
We investigate the potential benefits of interaction in a variety of classic learning theory topic areas, as well as the potential benefits that active learning can itself derive from these areas. Specifically, in the context of Bayesian learning, we find that access to a prior over target concepts can greatly benefit active learning algorithms, which are thereby enabled to significantly improv...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Computer Journal
سال: 2019
ISSN: 0010-4620,1460-2067
DOI: 10.1093/comjnl/bxz056