Supervised Model-based Classification Algorithm

A supervised model-based classification algorithm is a supervised classification algorithm that is a supervised model-based learning algorithm.

• Context:
  • It can be implemented into a model-based supervised classification system (to solve a model-based supervised classification task to produced a learned classification model).
  • It can range from being a Generative Classification Algorithm to being a Discriminative Classification Algorithm.
  • It can range from being a Supervised Model-based Binary Classification Algorithm to being a Supervised Model-based Multiclass Classification Algorithm.
  • It can range from being a Linear Classifier, a Quadratic Classifier, a Kernel-based Classifier, ..., depending on the classification model representation,
  • It can range from being a Statistical Classification Algorithm to being a Machine Learning Classification Algorithm.
• Example(s):
  • a Supervised Linear Classification Algorithm, such as: logistic regression, or a linear SVM.
  • Naive-Bayes Classification Algorithm.
  • CRF Algorithm.
  • Supervised Decision Tree Classification Algorithm.
  • a Neural Network-based Classification Algorithm.
  • …
• Counter-Example(s):
  • a Supervised Instance-based Classification Algorithm, such as a kNN classification algorithm.
  • a Supervised Model-based Estimation Algorithm, such as a linear regression algorithm.
• See: Supervised Online Classification Algorithm.

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References

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Statistical_classification Retrieved:2014-11-19.
  • … An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category.

    Terminology across fields is quite varied. In statistics, where classification is often done with logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features (grouped into a feature vector), and the possible categories to be predicted are classes. There is also some argumentover whether classification methods that do not involve a statistical model can be considered "statistical". Other fields may use different terminology: e.g. in community ecology, the term "classification" normally refers to cluster analysis, i.e. a type of unsupervised learning, rather than the supervised learning described in this article.

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