Understanding k-Nearest Neighbor: Definition, Explanations, Examples & Code
The k-Nearest Neighbor (kNN) algorithm is a simple instance-based algorithm used for both supervised and unsupervised learning. It stores all the available cases and classifies new cases based on a similarity measure. The algorithm is named k-Nearest Neighbor because classification is based on the k-nearest neighbors in the training set. kNN is a type of lazy learning algorithm, meaning that it doesn't have a model to t
Understanding Label Propagation Algorithm: Definition, Explanations, Examples & Code
The Label Propagation Algorithm (LPA) is a graph-based semi-supervised machine learning algorithm that assigns labels to previously unlabeled data points. LPA works by propagating labels from a subset of data points that are initially labeled to the unlabeled points. This is done throughout the course of the algorithm, with the labels being kept fixed unlike the closely related algorithm, label spreading. LPA i
Understanding Label Spreading: Definition, Explanations, Examples & Code
The Label Spreading algorithm is a graph-based semi-supervised learning method that builds a similarity graph based on the distance between data points. The algorithm then propagates labels throughout the graph and uses this information to classify unlabeled data points.
Label Spreading: Introduction
Domains
Learning Methods
Type
Machine Learning
Semi-Supervised
Graph-based
Label Spreading is a graph-based al
Understanding Semi-Supervised Support Vector Machines: Definition, Explanations, Examples & Code
Semi-Supervised Support Vector Machines (S3VM) is an extension of Support Vector Machines (SVM) for semi-supervised learning. It is an instance-based algorithm that makes use of a large amount of unlabelled data and a small amount of labelled data to perform classification tasks. The aim is to leverage the unlabelled data to improve the decision boundary constructed from the labelled data alone, whi
Understanding Stacked Auto-Encoders: Definition, Explanations, Examples & Code
Stacked Auto-Encoders is a type of neural network used in Deep Learning. It is made up of multiple layers of sparse autoencoders, with the outputs of each layer connected to the inputs of the next layer. Stacked Auto-Encoders can be trained using unsupervised or semi-supervised learning methods, making it a powerful tool for machine learning engineers to use in their work.
Stacked Auto-Encoders: Introduction
Do