A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

The past decade has witnessed many great successes of machine learning (ML) and deep
learning (DL) applications in agricultural systems, including weed control, plant disease …

This handbook provides a comprehensive and unified account of the main research
developments in cluster analysis. Written by active, distinguished researchers in this area …

Video semantic recognition usually suffers from the curse of dimensionality and the absence
of enough high-quality labeled instances, thus semisupervised feature selection gains …

Graphs have become increasingly popular in modeling structures and interactions in a wide
variety of problems during the last decade. Graph-based clustering and semi-supervised …

Category models for objects or activities typically rely on supervised learning requiring
sufficiently large training sets. Transferring knowledge from known categories to novel …

Graph based semi-supervised learning (SSL) methods play an increasingly important role in
practical machine learning systems. A crucial step in graph based SSL methods is the …

Graph clustering is an area in cluster analysis that looks for groups of related vertices in a
graph. Due to its large applicability, several graph clustering algorithms have been …

In this paper, we present a new analysis on co-training, a representative paradigm of
disagreement-based semi-supervised learning methods. In our analysis the co-training …