As one of the important research areas of multimodal interaction, sign language recognition (SLR) has attracted increasing interest. In SLR, especially on medium or large vocabula...
We advocate the use of Scaled Gaussian Process Latent Variable Models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a...
Raquel Urtasun, David J. Fleet, Aaron Hertzmann, P...
Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically ...
Activity Recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware wearable computing. However, most approaches for activity r...
In previous work [7] a computational framework was demonstrated that employs evolutionary algorithms to automatically model a given system. This is accomplished by alternating the...
In this paper, we propose a learning-based demosaicing and a restoration error detection. A Vector Quantization (VQ)based method is utilized for learning. We take advantage of a s...
Nowadays many data mining/analysis applications use the graph analysis techniques for decision making. Many of these techniques are based on the importance of relationships among t...
Rabia Nuray-Turan, Dmitri V. Kalashnikov, Sharad M...
The efficacy of Anomaly Detection (AD) sensors depends heavily on the quality of the data used to train them. Artificial or contrived training data may not provide a realistic v...
Gabriela F. Cretu, Angelos Stavrou, Michael E. Loc...
We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and ...
The Gaussian mixture model (GMM) can approximate arbitrary probability distributions, which makes it a powerful tool for feature representation and classification. However, it su...