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Finding an effective and efficient representation is very important for image classification. The most common approach is to extract a set of local descriptors, and then aggregate them into a high-dimensional, more semantic feature vector, like unsupervised bag-of-features and weakly supervised part-based models. The latter one is usually more discriminative than the former due to the use of information...
In this paper, we aim to address the issue that semi-supervised learning is prone to be influenced by the quality and quantity of initial seeds. In order to expand the initial labeled data, we select credible samples from unlabeled data by a proposed bilateral latent information miner. The miner can extract information from unlabeled data for both positive and negative class respectively. Then we...
This paper presents an enhanced version of descriptor DPM-BCF (Depth Projection Maps-based Bag of Contour Fragments). Named as eDPM, it modified the projection method by converting the depth cloud into three grayscale projected maps in three orthogonal planes. Then we extract Bag of Contour Fragments (BCF) descriptor and Histogram of Oriented Gradient (HOG) descriptor from the three grayscale projected...
The Fisher Vector (FV) is a very successful image representing method, which has achieved the state-of-the-art performance on scene classification. It concatenates the gradient of parameters in generative model as the image representation, which takes the advantage of generative and discriminative models. Using Gaussian mixture model (GMM) as the dictionary model, it can be regarded as an extension...
In China stock market, more than 95% are non-professional investors. Due to the lack of professional skill and the complexity of financial indicators and the varying investment environment, non-professional investors are in great need of a data mining-based intelligent stock trading decision-support system. Considering the existence of concept drift phenomenon, this study proposes an adaptive learning...
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