Various techniques are employed in each of the machine learning methods. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labelled training data consisting of a set of training examples. Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no preexisting labels and with a minimum of human supervision. In contrast to supervised learning that usually makes use of human-labelled data, unsupervised learning, also known as self-organising allows for modelling of probability densities over inputs. Semi-supervised learning is an approach to machine learning that combines a small amount of labelled data with a large amount of unlabelled data during training.
|Supervised Learning||Unsupervised Learning||Semi-Supervised Learning||Reinforcement Learning|
|Bayesian Statistics||Affinity Analysis||See Supervised Learning||Artificial Neural Networks (ANN)|
|Decision Trees||Clustering||Markov Decision Process (MDP)|
|Neural Networks||Nearest-Neighbour Mapping|
|Regression Analysis||Self-Organising Maps|
|Support Vector Machines||Singular Value Decomposition|