Abstract This paper proposes a novel approach to the measurement of linguistic diversity. Drawing on ecological literature, it an adaptation languages classic distinction between “alpha” (intra-setting), “beta” (inter-setting) and “gamma” (system-level) We begin by discussing some general challenges raised review received approaches diversity resting concepts richness evenness, highlighting lim...

Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and attractive. As one of most popular frameworks dealing with learning, performance k-nearest neighbor (kNN) algorithm mainly depends on metric used compute distance between different instances. In this paper, we propose novel cost-weighted geometric mea...

the performance of a decision making unit (dmu) can be evaluated in either across-sectional or a time-series manner, and data envelopment analysis (dea) is a useful method for both types of evaluation. the malmquist productivity index (mpi) evaluates the change in efficiency of a dmu between two time periods. it is defined as the product of the catch-up and frontier-shift terms. in this paper, ...

in recent years, different damage indexes have been introduced in engineering literature. the most prominent one among other counterparts is the 1985 park and ang's damage index (dipa), which demonstrates well calibration against experimental results. hence, it has traditionally had broad application in the field of structural engineering. commonly, in dipa relevant parameters are assessed...

Multi-modal data is dramatically increasing with the fast growth of social media. Learning a good distance measure for data with multiple modalities is of vital importance for many applications, including retrieval, clustering, classification and recommendation. In this paper, we propose an effective and scalable multi-modal distance metric learning framework. Based on the multi-wing harmonium ...

In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fixed size. The Slim-tree uses the "fat-factor" which provides a simple way to quantify the degree of overlap between the nodes in a metric tree. It is well-known that the degree of overlap directly affects the query performance of index structures. There are many suggestions to reduce overlap in m...