Image classification is a key task in image understanding, and multi-label image
classification has become a popular topic in recent years. However, the success of multi …

Learning from positive and unlabeled data or PU learning is the setting where a learner only
has access to positive examples and unlabeled data. The assumption is that the unlabeled …

Text classification is one of the important research issues in the field of text mining, where
the documents are classified with supervised knowledge. In literature we can find many text …

Transfer learning aims to provide a framework to utilize previously-acquired knowledge to
solve new but similar problems much more quickly and effectively. In contrast to classical …

A major assumption in many machine learning and data mining algorithms is that the
training and future data must be in the same feature space and have the same distribution …

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents
an accessible treatment of the two main components of support vector machines (SVMs) …

The input to an algorithm that learns a binary classifier normally consists of two sets of
examples, where one set consists of positive examples of the concept to be learned, and the …

Spammers, who manipulate online reviews to promote or suppress products, are flooding in
online commerce. To combat this trend, there has been a great deal of research focused on …

Recently, multi-label learning has received much attention in the applications of image
annotation and classification. However, most existing multi-label learning methods do not …

Long-term cycle life test in battery development is crucial for formulations selection but time-
consuming and high-cost. To shorten cycle test with estimated lifespan, a prediction-based …