Supervised Learning System

A Supervised Learning System is a machine learning system implements a supervised learning algorithm to solve a supervised learning task (to learn to predict an output given an input dataset).

• AKA: Supervised Model Learner, Predictive Modeling Software.
• Context:
  • It can range from being a Supervised Class Prediction System, to being a Supervised Rank Prediction System, to being a Supervised Numeric-Value Prediction System.
  • It can range from being an Expert-operated Predictive Modeling System to being an Automated Predictive Modeling System.
  • It can range from being a Predictive Modeling Software System to being a Predictive Modeling Service.
  • It can range from being a Single-Model Supervised Learning System to being an Ensemble Supervised Learning System.
  • ...
• Example(s):
  • a Locally Weighted Learning System.
  • an Instance-Based Learning System.
  • a Regression Learning System such as:
    • a Linear Regression System.
    • a Support Vector Regression System.
    • a Ensemble Learning System.
    • a Decision Tree-based Learning System, such as one that uses C4.5 or R's rpart.
    • a Generalized Linear Model-based System, such as R GLM.
  • a Classification Learning System such as:
    • a Support Vector Machine-based System, such as one that uses SVMlight and LIBSVM.
    • a Word Vectorizing Model Training System.
    • a Nearest Neighbor Classification System.
  • a Supervised Artificial Neural Network such as:
    • a Simple Recurrent Network.
    • a Feed-Forward Neural Network (Backpropagation).
    • a Radial Basis Function Network.
    • a Cascade-Correlation Network.
  • …
• Counter-Example(s):
  • an Unsupervised Learning System.
  • a Reinforcement Learning System.
  • a Semi-supervised Learning System.
• See: kNN System, Supervised Learning Tool, Discriminant Analysis, Naive Bayes .

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References

2017A

• (Sammut & Webb, 2017) ⇒ Supervised Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA pp. 1213-1214
  • QUOTE: Supervised learning refers to any machine learning process that learns a function from an input type to an output type using data comprising examples that have both input and output values. Two typical examples of supervised learning are classification learning and regression. In these cases, the output types are respectively categorical (the classes) and numeric. Supervised learning stands in contrast to unsupervised learning, which seeks to learn structure in data, and to reinforcement learning in which sequential decision-making policies are learned from reward with no examples of “correct” behavior.

2017B

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Supervised_learning Retrieved:2017-12-24.
  • Supervised learning is the machine learning task of inferring a function from. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).

    The parallel task in human and animal psychology is often referred to as concept learning.

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