… This document introduces the paradigm of supervised learning. It also discusses nearest
neighbor classification and the distance functions necessary for nearest neighbor classification. …

… Learning that has also made its mark in the field of research. Its main constituent Supervised
Learning is … A small introductory part of unsupervised learning along with reinforcement …

… learning and many supervised learning techniques have found application in the processing
of multimedia content. The defining characteristic of supervised learn… the learning system on …

… The success of semi-supervised learning … -supervised learning for cognitive psychology.
Finally, we give a computational learning theoretic perspective on semisupervised learning, and …

… supervised learning with some examples to comprehend the idea followed by different types
of Supervised Learning … commonly used algorithms of supervised learning and their efficacy …

… techniques construct predictive models by learning from a … learning techniques to work with
weak supervision. This article reviews some research progress of weakly supervised learning…

… , semi-supervised learning algorithms aim to find a decision function f : X → {±1} and a label
… aspects of the safeness problem in semi-supervised learning, which we will discuss in this …

… focus on lifelong learning paradigm in context of supervised learning. We briefly recap the
supervised learning setup (Section 2.2), describe the lifelong supervised learning paradigm (…

… 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 a lot of research …

… emi-supervised learning is a class of supervised learning tasks and techniques that also
make use of unlabeled data for training - typically a small amount of labeled data with a large …