Supervised learning definition

Supervised learning means the machine learning task of learning a function that maps from an input to an output based on labelled input-output pairs.90
Supervised learning means that humans train machines by feeding them labelled input and output data: in other
Supervised learning. Supervised learning uses a set of input data with known responses to the data (output) to generate a model of the perceived reality, with the aim to then generate reasonable predictions as a response to new data

Examples of Supervised learning in a sentence

  • Including: • Supervised learning, where observations contain input/output pairs (also known as "labelled data"); • Unsupervised learning (where labels are omitted); and • Reinforcement learning (where evaluations are given of how good or bad a situation is).

  • Supervised learning experience in an industry setting is an important educational component for many professional technical programs.

  • Including:  Supervised learning, where observations contain input/output pairs (also known as "labelled data");  Unsupervised learning (where labels are omitted); and  Reinforcement learning (where evaluations are given of how good or bad a situation is).

  • Supervised learning from game records has been quite successful, and is used in some top-level Go programs such as Zen or Crazy Stone.

  • Supervised learning is the most common method thus far, and is suitable for training an algorithm in the laboratory.

  • Supervised learning algorithms use the annotation guidance in the training data to draw conclusions about the input data.

  • Supervised learning has been extensively utilized for channel estimation and spectrum sensing.

  • Supervised learning and hybrid intelligence approaches are the more extensive and costly methods but result in more qualitative systems that are relatively better in problem solving techniques and capable of achieving their goals much faster (▇▇▇▇▇▇▇▇▇ & ▇▇▇▇▇▇, 2017; ▇▇▇▇▇▇ et al., 2017).

  • To compare RL with the most popular categories in current machine learning research field, i.e., supervised learning and unsupervised learning, we give a general review of them, as follows • Supervised learning learns from a training set of labeled examples to model relationships and dependencies between the target prediction output and the input features [92].

  • Supervised learning can be realized through different algorithms such as decision tree, support vector machines, etc.


More Definitions of Supervised learning

Supervised learning means that humans train machines by feeding them labelled input and output data: in other words, humans show AI-based applications what they are expected to do.