Using Large Datasets to Understand Nanotechnology

Using large SNP datasets to understand the genetic mechanisms of complex traits in Arabidopsis thaliana

Learning using Large Datasets

This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of small-scale and large-scale learning problems. Small-scale learning problems are subject to the usual approximation– estimation tradeoff. Large-scale learning problems are subject to a qualitatively differ...

Clustering Large Dynamic Datasets Using Exemplar Points

In this paper we present a method to cluster large datasets that change over time using incremental learning techniques. The approach is based on the dynamic representation of clusters that involves the use of two sets of representative points which are used to capture both the current shape of the cluster as well as the trend and type of change occuring in the data. The processing is done in a...

Interacting with Large Distributed Datasets Using Sketch

We present Sketch, a library and a distributed runtime for building interactive tools for exploring large datasets, distributed across multiple machines. We have built several sophisticated applications using this framework; in this paper we describe a billion-row spreadsheet, and a distributed-systems performance analyzer. Sketch applications allow interactive and responsive exploration of com...

Final Report: Object Recognition Using Large Datasets

Object recognition is a difficult problem due to the large feature space and the complexity of feature dependencies. First, there exist positional complexities resulting from the 3D position and orientation of the object as well as the 3D position and orientation of the camera. Further, changes in lighting, background, and occlusion can create dramatically different images for the same object. ...