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Reinforcement learning is an important method of machine learning. This paper using the graph theory to express varieties of knowledge points, which their's relationship is expressed by the graph of topological graph. Applied the Technology of association rule Recommendation to deal with the relationship between these knowledge points, give the corresponding of the recommendation work flow chart....
In this paper, for every local feature, we propose to learn its similar local features across all positive images, instead of using heuristic distance as similarity measure. Specifically, multiple instance learning (MIL) is employed to simultaneously determine the similar points of a local feature and learn its corresponding discriminative function which can be regarded as some kind of similarity...
As an emerging human-computer interaction approach vision based hand interaction is more natural and efficient. However in order to achieve high accuracy, most of the existing hand posture recognition methods need a large number of labeled samples which is expensive or unavailable in practice. In this paper, a co-training based method is proposed to recognize different hand postures with a small quantity...
Scene is a series of semantic correlated video shots. An effective scene detection depends on domain knowledge more or less. Most existing approaches try to directly detect various scene changes by applying clustering or supervised learning methods to low level audiovisual features. However, robustly detecting diverse scene changes derived from complex semantic meanings is still a challenging problem...
This paper presents a novel algorithm named diverse AdaBoostSVM tracking (DABSVT) for target tracking in infrared imagery. The tracker trains a support vector machine (SVM) classifier per frame. All of the classifiers are combined into an ensemble classifier using AdaBoost. By proper parameter adjusting strategies, a set of effective SVM classifiers with moderate accuracy are obtained, and the dilemma...
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