Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …

We present a theory of ensemble diversity, explaining the nature of diversity for a wide range
of supervised learning scenarios. This challenge has been referred to as the" holy grail" of …

Efficient hashing techniques have attracted extensive research interests in both storage and
retrieval of high-dimensional data, such as images and videos. In existing hashing methods …

Distance metric learning (DML), which learns a distance metric from labeled" similar" and"
dissimilar" data pairs, is widely utilized. Recently, several works investigate orthogonality …

Distance metric learning (DML) is to learn a representation space equipped with a metric,
such that similar examples are closer than dissimilar examples concerning the metric. The …

In healthcare, a tsunami of medical data has emerged, including electronic health records,
images, literature, etc. These data are heterogeneous and noisy, which renders clinical …

In the field of machine learning, the training of an ensemble of models is a very common
method for reducing the variance of the prediction, and yields better results. Many …

The linear model is commonly utilized in hashing methods owing to its efficiency. To obtain
better accuracy, linear-based hashing methods focus on designing a generalized linear …

Hashing aims at learning discriminative binary codes of high-dimensional data for the
approximate nearest neighbor searching. However, the distance ranking obtained by …

With the rising number of machine learning competitions, the world has witnessed an
exciting race for the best algorithms. However, the involved data selection process may …