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In this research we present a new framework and associated algorithms for mining high speed data streams that take advantage of concept recurrence. Different from previous work our approach detects volatility in a stream and then matches the learning paradigm to the degree of volatility. In high volatility stream segments a decision forest is used as the learning mechanism whereas in low volatility...
The application of current drift detection methods to real data streams show trends in the rate of change found by the detectors. We observe that these patterns of change vary across different data streams. We use the term stream volatility pattern to describe change rates with a distinct mean and variance. First, we propose a novel drift prediction algorithm to predict the location of future drift...
The threat of false data injection (FDI) attacks have raised wide interest in the research and development of smart grid security. This paper presents a comparative study on the utilization of supervised learning classifiers to detect direct and stealth FDI attacks in the smart grid. A detailed formulation of the problem for detection with classifiers is first described with proper assumptions and...
Online mining is a difficult task especially when such data streams evolve over time. Evolving data stream occurs when concepts drift or change completely, is becoming one of the core issues. A large portion of change detection research are carried out in the area of supervised learning, very little has been carried out for unlabeled data specifically in the area of transactional data streams. Overall...
Changes in the data distribution (concept drift) makes online learning a challenge that is progressively attracting more attention. This paper proposes Boosting-like Online Learning Ensemble (BOLE) based on heuristic modifications to Adaptable Diversity-based Online Boosting (ADOB), which is a modified version of Oza and Russell's Online Boosting. More precisely, we empirically investigate the effects...
The recent success of representation learning is built upon the learning of relevant features, in particular from unlabelled data available in different domains. This raises the question of how to transfer and reuse such knowledge effectively so that the learning of a new task can be made easier or be improved. This poses a difficult challenge for the area of transfer learning where there is no label...
Object detection from still images has been among the most active and challenging area in computer vision recently. In contrast, fully supervised object detection from video has rarely been investigated. In this paper, we propose an algorithm to improve the performance of object detection from video. Our proposed method is based on an empirical property that the trajectory of an object is important...
Capturing large diffeomorphic deformations is difficult for many non-rigid registration methods. In this paper, we propose Log-Demons with driving force for large deformation image registration. The driving force obtained by boundary points correspondence exerts influence on continuous optimization of Log-Demons to improve the motion direction of points. We utilize MROGH descriptor matching to obtain...
Research on pedestrian detection still presents a lot of space for improvements, both on speed and detection accuracy. State-of-the-art object proposals approach has shown the very effective computational efficiency in object detection. In this paper, we present a framework for pedestrian detection based on the object proposals. Instead of scaling the test image to different sizes, we generate a pyramid...
The fly visual system, although tiny when compared to the mammalian visual system, can still perform highly sophisticated spatial tasks like collision avoidance, landing on objects, pursuit of prey, etc. Flies outperform human-made autonomous flying systems in solving such spatial tasks by a long way. This is partly due to their ability to perceive and respond to optical flow generated by motion in...
In nature, it is an important task for animals to detect small targets which move within cluttered background. In recent years, biologists have found that a class of neurons in the lobula complex, called STMDs (small target motion detectors) which have extreme selectivity for small targets moving within visual clutter. At the same time, some researchers assert that lateral inhibition plays an important...
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