Author(s) Name: Taiwo Oladipupo Ayodele
Supervised learning --- where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function.
Unsupervised learning --- which models a set of inputs: labeled examples are not available.
Semi-supervised learning --- which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
Reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. •Transduction --- similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs.
Learning to learn --- where the algorithm learns its own inductive bias based on previous experience.
Table of Contents
ISBN: 978-953-307-034-6
Publisher: InTech, Publisher
Year of Publication: 2010
Book Link: Home Page Url
