Visual learning is expected to be a continuous and robust process, which treats input images and pixels selectively. In this paper we present a method for subspace learning, which...
This paper proposes a metric learning based approach for human activity recognition with two main objectives: (1) reject unfamiliar activities and (2) learn with few examples. We s...
We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithm...
The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum...
Video Compression currently is dominated by engineering and fine-tuned heuristic methods. In this paper, we propose to instead apply the well-developed machinery of machine learni...