The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

Existing active learning studies typically work in the closed-set setting by assuming that all
data examples to be labeled are drawn from known classes. However, in real annotation …

This paper investigates the patient risk prediction problem in the context of active learning
with relative similarities. Active learning has been extensively studied and successfully …

In dyadic prediction, labels must be predicted for pairs (dyads) whose members possess
unique identifiers and, sometimes, additional features called side-information. Special cases …

The problem of similarity learning is relevant to many data mining applications, such as
recommender systems, classification, and retrieval. This problem is particularly challenging …

Pattern mining provides useful tools for exploratory data analysis. Numerous efficient
algorithms exist that are able to discover various types of patterns in large datasets …

Active learning aims to reduce labeling efforts by selectively asking humans to annotate the
most important data points from an unlabeled pool and is an example of human-machine …

Semantic attributes have been recognized as a more spontaneous manner to describe and
annotate image content. It is widely accepted that image annotation using semantic …

In this paper we propose a new set of primitives to realize a large-area covering realistic
tactile display. They stimulate the skin surface with suction pressure (SPS method) as our …

While ranking is widely used in many online domains such as search engines and
recommendation systems, it is non-trivial to label enough data examples to build a high …