While GANs have shown success in realistic image generation, the idea of using GANs for
other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural …

Based on data types, four learning methods have been presented to extract patterns from
data: supervised, semi-supervised, unsupervised, and reinforcement. Regarding machine …

Cross-modal hashing maps heterogeneous multimedia data into a common Hamming
space to realize fast and flexible cross-modal retrieval. Supervised cross-modal hashing …

Nowadays, the application of data mining is widely prevalent in the education system. The
ability of data mining to obtain meaningful information from meaningless data makes it very …

Abstract Develo** effective invasive Ductal Carcinoma (IDC) detection methods remains a
challenging problem for breast cancer diagnosis. Recently, there has been notable success …

Traditional computer aided detection (CAD) systems for breast cancer screening relied on
machine learning with human-coded feature-engineering. They have largely failed to fulfill …

Semi‐supervised learning is a machine learning paradigm which combines both labeled
and unlabeled data to increase the performance accuracy of the machine. Unlike the …

Semi-supervised learning improves the performance of supervised machine learning by
leveraging methods from unsupervised learning to extract information not explicitly available …

One major challenge for machine learning in genomics is the scarcity of labeled data. In
order to obtain high quality labels, it is often necessary to perform expensive experiments …

Data streams are important sources of information nowadays, and with the popularization of
mobile devices and sensor systems that collect all kinds of data, more and more information …