Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an...

approaches to machine learning relying on explanation-based learning, such as PROTOS, made use of explicit representations of explanations expressed in a dedicated...

compromise between exemplar and prototype models. The basic idea of explanation-based learning suggests that a new concept is acquired by experiencing examples...

computational biology One-shot learning in computer vision Transfer learning Fast mapping Explanation-based learning Xian, Yongqin; Lampert, Christoph...

Inquiry-based learning (also spelled as enquiry-based learning in British English) is a form of active learning that starts by posing questions, problems...

page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of artificial...

Problem-based learning (PBL) is a teaching method in which students learn about a subject through the experience of solving an open-ended problem found...

its subfield of machine learning, a right to explanation (or right to an explanation) is a right to be given an explanation for an output of the algorithm...

task-based learning processes. According to Jon Larsson, in considering problem-based learning for language learning, i.e., task-based language learning:...

achievement in the greater field of music learning theory. The theory is an explanation of music learning, based on audiation (see below) and students' individual...

(model-based) learning effective distance metrics (metrics-based) explicitly optimizing model parameters for fast learning (optimization-based). Model-based...

person's height based on factors like age and genetics or forecasting future temperatures based on historical data. Similarity learning is an area of supervised...

learning) Explanation-based learning Feature GloVe Hyperparameter Inferential theory of learning Learning automata Learning classifier system Learning rule...

agent-based modeling (ABM) community has developed several practical agent based modeling toolkits that enable individuals to develop agent-based applications...

behaviour, promoting a more active form of learning than reading or listening. The term "explorable explanation" was first used in passing by Peter Brusilovsky...

Discovery learning is a technique of inquiry-based learning and is considered a constructivist based approach to education. It is also referred to as problem-based...

the cause of death of the racehorse Swale inspired the SWALE explanation-based learning system which detects and explains anomalies in horses. Swale posthumously...

Evidence-based education is related to evidence-based teaching, evidence-based learning, and school effectiveness research. The evidence-based education...

Occami) is the problem-solving principle that recommends searching for explanations constructed with the smallest possible set of elements. It is also known...

learned. Evidence-based learning is the use of evidence from well designed scientific studies to accelerate learning. Evidence-based learning methods such...

Predictors". Machine Learning. 37 (3): 297–336. doi:10.1023/A:1007614523901. S2CID 2329907. Zhou, Zhihua (2008). "On the margin explanation of boosting algorithm"...

Big data Explanation-based learning List of datasets for machine learning research Predictive analytics Seq2seq Fairness (machine learning) Embedding...

Electron-beam lithography Electronic brakeforce limitation Explanation-based learning, a form of machine learning Extragalactic background light, all the accumulated...

D. O. Hebb proposed a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning. It was used in many early...

using logic Satplan Learning using logic Inductive logic programming Explanation based learning Relevance based learning Case based reasoning General logic...

In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence...

Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based on various...

co-director of the Robot Learning Laboratory at CMU. As a faculty member at CMU, he co-founded the Master's Program in Automated Learning and Discovery, which...

machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major...

to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field...