Passive Learning Task

AKA: Passive Machine Learning Task, Static Learning.
Context:
- It is solved by Passive Learning System by implementing Passive Learning Algorithms.
Example(s):
- Passive Reinforcement Learning Task,
- PAC Learning,
- …
Counter-Example(s):
- Active Learning Task,
- Deep Learning Task.
See: Machine Learning System, Training Data, Passive Learning (Teaching Method).

References

(Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Passive_learning Retrieved:2019-5-21.
- Passive learning is a method of learning or instruction where students receive information from the instructor and internalize it, and "where the learner receives no feedback from the instructor". ^[1] ^[2] The term is often used together with direct instruction and lecturing, with passive learning being the result or intended outcome of the instruction. This style of learning is teacher-centered and contrasts to active learning, which is student-centered, whereby students take an active or participatory role in the learning process, and to the Socratic method where students and instructors engage in cooperative argumentative dialogue. Passive learning is a traditional method utilized in factory model schools and modern schools, as well as historic and contemporary religious services in churches (sermons), mosques, and synagogues. Passive learning is not simply the outcome of an educational model. Passive learners may quietly absorb information and knowledge without typically engaging with the information received or the learning experience. They may not interact with others, share insights, or contribute to a dialogue. An estimated 60 percent of people are passive learners. ^[3]

(Balcan & Long, 2013) ⇒ Maria Florina Balcan, and Philip M. Long. (2013). “Active and Passive Learning of Linear Separators under Log-concave Distributions.” In: Proceedings of JMLR: Workshop and Conference (2013).
- QUOTE: Passive Learning - In the classic passive supervised machine learning setting, the learning algorithm is given a set of labeled examples drawn i.i.d. from some fixed but unknown distribution over the instance space and labeled according to some fixed but unknown target function, and the goal is to output a classifier that does well on new examples coming from the same distribution.