The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Support vector machine (SVM) is an optimal margin based classification technique in
machine learning. SVM is a binary linear classifier which has been extended to non-linear …

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 …

It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …

Extreme learning machines (ELMs) have proven to be efficient and effective learning
mechanisms for pattern classification and regression. However, ELMs are primarily applied …

(57) ABSTRACT A method of modelling data, comprising: training an objec tive function of a
linear classifier, based on a set of labeled data, to derive a set of classifier weights; defining …

Background Studies have demonstrated that the current US guidelines based on American
College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations …

In many real-world applications, we are often facing the problem of cross domain learning,
ie, to borrow the labeled data or transfer the already learnt knowledge from a source domain …

Real-time driver distraction detection is the core to many distraction countermeasures and
fundamental for constructing a driver-centered driver assistance system. While data-driven …

Many graph-based semi-supervised learning methods for large datasets have been
proposed to cope with the rapidly increasing size of data, such as Anchor Graph …