Abstract
Supervised learning involves training using well “labelled” training data, and on the basis of the training data, machines are able to predict the output. The labelled data means that the correct output is attached along with the corresponding input data. In supervised learning, as the name suggests, the training data acts as the supervisor and provides training to the machine to predict the correct output. This chapter discusses Statistical Decision Theory, Gaussian & Normal Distribution, Conditionally Independent Binary Components, Learning Beliefs Network and Nearest-Neighbour Methods.
Keywords: Belief networks, Normal distribution, Statistical decision theory, Supervised learning.
Related Journals
Recent Patents on Computer Science
Current E-Learning
Current Computer Science
International Journal of Sensors, Wireless Communications and Control
Recent Advances in Computer Science and Communications
Journal of Fuzzy Logic and Modeling in Engineering
The Chinese Journal of Artificial Intelligence
Current Chinese Computer Science
Current Artificial Intelligence
Related Books
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
A First Course in Artificial Intelligence
Artificial Intelligence: Models, Algorithms and Applications
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
Handbook of Mobile Application Development: A Guide to Selecting the Right Engineering and Quality Features
Applications of Modern High Performance Networks
Arduino Meets Matlab: Interfacing, Programs and Simulink
Arduino and SCILAB based Projects
Application of Chaos and Fractals to Computer Vision