In the last decade a large number of supervised learning methods have been introduced in the field of the machine learning. Supervised learning became an area for research activity in machine learning. Many of the supervised learning techniques have found application in their processing and analyzing variety of data. One of the main characteristics is that the supervised learning has the ability of annotated training data. The so called labels are class labels in the classification process. There is a variety of algorithms that are used in the supervised learning methods. This paper summarizes the fundamental aspects of couple of supervised methods.
Key Points
Supervised learning is an area of machine learning concerned with building models from example data sets
Supervised learning is used in applications where historical data is used to predict future events
Supervised learning algorithms generate a function that maps inputs to desired outputs
Supervised learning models are divided into two categories: classification and regression
Classification models map the input space into predetermined classes, while regression models map the input space into a real-value domain
Title
An overview of supervised machine learning methods
URL
https://www.researchgate.net/profile/Vladimir-Nasteski/publication/328146111_An_overview_of_the_supervised_machine_learning_methods/links/5c1025194585157ac1bba147/An-overview-of-the-supervised-machine-learning-methods.pdf
Summary
In the last decade a large number of supervised learning methods have been introduced in the field of the machine learning. Supervised learning became an area for research activity in machine learning. Many of the supervised learning techniques have found application in their processing and analyzing variety of data. One of the main characteristics is that the supervised learning has the ability of annotated training data. The so called labels are class labels in the classification process. There is a variety of algorithms that are used in the supervised learning methods. This paper summarizes the fundamental aspects of couple of supervised methods.
Key Points
Supervised learning is an area of machine learning concerned with building models from example data sets Supervised learning is used in applications where historical data is used to predict future events Supervised learning algorithms generate a function that maps inputs to desired outputs Supervised learning models are divided into two categories: classification and regression Classification models map the input space into predetermined classes, while regression models map the input space into a real-value domain
Citation
Repo link