Better and Faster Patient Training

Better, faster, lighter Java

Preparation and training: the key to better patient involvement.

Articles which compare practical examples of diVerent methods of involving users are rare, so the paper by van Wersch and Eccles in this issue of Quality in Health Care is of particular interest. It examines four methods of involving patients in the development of clinical guidelines which, in itself, is an area that has received relatively little attention. The four methods tested were: + incl...

Sparser, Better, Faster GPU Parsing

Due to their origin in computer graphics, graphics processing units (GPUs) are highly optimized for dense problems, where the exact same operation is applied repeatedly to all data points. Natural language processing algorithms, on the other hand, are traditionally constructed in ways that exploit structural sparsity. Recently, Canny et al. (2013) presented an approach to GPU parsing that sacri...

Faster (and Better) Entity Linking with Cascades

Entity linking requires ranking thousands of candidates for each query, a time consuming process and a challenge for large scale linking. Many systems rely on prediction cascades to efficiently rank candidates. However, the design of these cascades often requires manual decisions about pruning and feature use, limiting the effectiveness of cascades. We present Slinky, a modular, flexible, fast ...

A Method to Generate a Reduced Training Set for Faster and Better Nearest Neighbor Classification

Classification time and space requirements of nearest neighbor based classifiers depends directly on the training set size. There exist several ways to reduce the training set size, and also there exist some methods to generate artificial training sets which are aimed at achieving better classification accuracy. These are often called bootstrap methods. The paper proposes a method which tries t...

Better, Faster, Stronger: Building a Better Dublin Core Generator