To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a given data space and train SVMs on the decomposed regions. Although the...
Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen L...
The self-organising map (SOM) has been successfully employed as a nonparametric method for dimensionality reduction and data visualisation. However, for visualisation the SOM requ...
Every stereovision application must cope with the correspondence problem. The space of the matching variables, often consisting of spatial coordinates, intensity and disparity, is...
Abstract. The deployment of Share Data Spaces in open, possibly hostile, environments arises the need of protecting the confidentiality of the data space content. Existing approach...
Giovanni Russello, Changyu Dong, Naranker Dulay, M...
In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spaces. The PyramidTechnique is highly adapted to range query processing using the...
Factorization using Singular Value Decomposition (SVD) is often used for recovering 3D shape and motion from feature correspondences across multiple views. SVD is powerful at find...
We present a novel clustering method using HMM parameter space and eigenvector decomposition. Unlike the existing methods, our algorithm can cluster both constant and variable leng...
The shared data space model has proven to be an effective paradigm for building distributed applications. However, building an efficient distributed implementation remains a chall...
Giovanni Russello, Michel R. V. Chaudron, Maarten ...
Query processing on mobile sensor networks requires efficient indexing and partitioning of the data space to support efficient routing as the network scales up. Building an index ...
Abstract. We introduce SONAR, a structured overlay to store and retrieve objects addressed by multi-dimensional names (keys). The overlay has the shape of a multi-dimensional torus...