Active Learning System

Context:
- It implements an Active Learning Task to solve a Active Learning Task.
- …
Example(s):
- ANALYTiC (Junior et al., 2017),
- NEXT (Jamieson et al., 2015),
- a Constructive Active Learning System,
- a Pool-Based Active Learning System,
- a Stream-Based Active Learning System,
- a Statistical Active Learning System.
- an ASE-Progol System.
- …
Counter-Example(s)
See: Interactive Entity Record Disambiguation System, Active Learning Theory, Machine Learning System.

References

(Cohn, 2017) ⇒ Cohn D. (2017) Active Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
- QUOTE: The term Active Learning is generally used to refer to a learning problem or system where the learner has some role in determining on what data it will be trained. This is in contrast to Passive Learning, where the learner is simply presented with a training set over which it has no control. Active learning is often used in settings where obtaining labeled data is expensive or time-consuming; by sequentially identifying which examples are most likely to be useful, an active learner can sometimes achieve good performance, using far less training data than would otherwise be required.

(Dasgupta, 2017) ⇒ Dasgupta S. (2017) Active Learning Theory. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
- QUOTE: The term active learning applies to a wide range of situations in which a learner is able to exert some control over its source of data. For instance, when fitting a regression function, the learner may itself supply a set of data points at which to measure response values, in the hope of reducing the variance of its estimate. Such problems have been studied for many decades under the rubric of experimental design (Chernoff 1972; Fedorov 1972). More recently, there has been substantial interest within the machine learning community in the specific task of actively learning binary classifiers. This task presents several fundamental statistical and algorithmic challenges, and an understanding of its mathematical underpinnings is only gradually emerging. This brief survey will describe some of the progress that has been made so far.

(Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Active_learning_(machine_learning) Retrieved:2017-12-24.
- Active learning is a special case of semi-supervised machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature it is sometimes also called optimal experimental design.
  There are situations in which unlabeled data is abundant but manually labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm be overwhelmed by uninformative examples.
  Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning.