This paper surveys locally weighted learning, a form of lazy learning and memory-based
learning, and focuses on locally weighted linear regression. The survey discusses distance …

The¡-means algorithm is by far the most widely used method for discovering clusters in data.
We show how to accelerate it dramatically, while still always computing exactly the same …

The k-means algorithm and its variations are known to be fast clustering algorithms.
However, they are sensitive to the choice of starting points and are inefficient for solving …

In this paper, fast search algorithms are proposed and studied for vector quantization
encoding using the K-dimensional (Kd) tree structure. Here, the emphasis is on the optimal …

In this paper we address the problem of building object class representations based on local
features and fast matching in a large database. We propose an efficient algorithm for …

The availability of packaged clustering programs means that anyone with data can easily do
cluster analysis on it. But many users of this technology don't fully appreciate its many …

Bayesian inference using Gaussian processes on large datasets have been studied
extensively over the past few years. However, little attention has been given on how to apply …

In distributed storage systems, documents are shared among multiple Cloud providers and
stored within their respective storage servers. In social secret sharing-based distributed …

Rapid advances in speech recognition theory, as well as computing hardware, have led to
the development of machines that can take human speech as input, decode the information …

This dissertation tackles the complexities of characterizing flow and particle dynamics in
constrained environments by advancing 3D particle tracking techniques at micro and meso …