Land-cover map** in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

Online algorithms that process one example at a time are advantageous when dealing with
very large data or with data streams. Stochastic Gradient Descent (SGD) is such an …

Nowadays, datasets are always dynamic and patterns in them are changing. Instances with
different labels are intertwined and often linearly inseparable, which bring new challenges to …

Abstract Support Vector Machines (SVMs) have gained outstanding generalization in many
fields. However, standard SVM and most of modified SVMs are in essence batch learning …

Recently, semi-supervised federated learning (semi-FL) has been proposed to handle the
commonly seen real-world scenarios with labeled data on the server and unlabeled data on …

Federated learning (FL) enables multiple clients to collaboratively train a global model
without disclosing their data. Previous researches often require training the complete model …

In real applications, it is difficult to obtain a sufficient number of training samples in
supervised classification of hyperspectral remote sensing images. Furthermore, the training …

The causative agents of most coral diseases today remain unknown, complicating disease
response and restoration efforts. Pathogen identifications can be hampered by complex …

This paper proposes twin prototype support vector machine (TVM), a constant space and
sublinear time support vector machine (SVM) algorithm for online learning. TVM achieves its …

Support vector data description (SVDD) is a well-known technique for one-class
classification problems. However, it incurs high time complexity in handling large-scale …