High-throughput genomic and proteomic technologies are widely used in cancer research to
build better predictive models of diagnosis, prognosis and therapy, to identify and …

Increasingly large data series collections are becoming commonplace across many different
domains and applications. A key operation in the analysis of data series collections is …

We consider the problem of zero-shot learning, where the goal is to learn a classifier $ f:
X\rightarrow Y $ that must predict novel values of $ Y $ that were omitted from the training …

The area of similarity searching is a very hot topic for both research and c-mercial
applications. Current data processing applications use data with c-siderably less structure …

Nearest neighbor search and many other numerical data analysis tools most often rely on
the use of the euclidean distance. When data are high dimensional, however, the euclidean …

This paper presents\bic (B order/I nterior pixel C lassification), a compact and efficient CBIR
approach suitable for broad image domains. It has three main components:(1) a simple and …

Nearest neighbor (NN) search in high dimensional space is an important problem in many
applications. Ideally, a practical solution (i) should be implementable in a relational …

Content-based copy retrieval (CBCR) aims at retrieving in a database all the modified
versions or the previous versions of a given candidate object. In this paper, we present a …

The problem of efficiently finding the best match for a query in a given set with respect to the
Euclidean distance or the cosine similarity has been extensively studied. However, the …

Data series are a special type of multidimensional data present in numerous domains,
where similarity search is a key operation that has been extensively studied in the data …