Face Recognition has rapidly grown as a commercial requirement for a variety of
applications in recent years. There are certain situations in which all the face images may …

Unmanned aerial vehicle (UAV) has become an important radar target recently because of
its wide applications and potential security threats. Traditionally, visual features such as …

In streaming data classification, most of the existing methods assume that all arrived
evolving data are completely labeled. One challenge is that some applications where only …

High dimensional data classification with very limited labeled training data is a challenging
task in the area of data mining. In order to tackle this task, we first propose a feature …

With the massive explosion of machine learning in our day-to-day life, incremental and
adaptive learning has become a major topic, crucial to keep up-to-date and improve …

Semisupervised learning methods are often adopted to handle datasets with very small
number of labeled samples. However, conventional semisupervised ensemble learning …

Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled
source domain to help the task of target domain with the unlabeled data. It is a key step for …

In this paper, a combined framework is proposed that includes Hyperdimensional (HD)
computing, neural networks, and k-means clustering to fulfill a computationally simple …

Incremental and decremental problems are challenging tasks in semi-supervised learning.
The incremental semi-supervised discriminant analysis (ISSDA) method proposed by …

Semisupervised scheme has emerged as a popular strategy in the machine learning
community due to the expensiveness of getting enough labeled data. In this paper, a …