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In real world classification tasks, the original instances are represented by raw features. Usually domain related algorithms are needed to extract discriminative features. But the algorithms selection and additional parameters tuning are difficult for people with little domain knowledge and experience. In this paper, a new machine learning framework called "decompose learning" is proposed...
In a SVM classifier, the training speed is sensitive to the quantity of dataset. Therefore, the methodology of choosing some useful data that can decrease the number of training data and accelerate the training speed is usually a topic to be discussed on the SVM data process. The hyperplane of SVM is constructed by a small number of vectors. These vectors, whose locations are distributed in other...
We propose a semi-supervised method of land cover classification for remotely sensed multispectral data. The method is useful especially when the number of training data is small and restricted. The method derives the additional training data out of the object image by using the results of two different types of classifiers. We extract the pixels in which the results of two classifiers were coincide...
Multicolor fluorescence in situ hybridization (M-FISH) techniques provide color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Chromosomes are stained combinatorially in M-FISH. By analyzing the intensity combinations of each pixel, all chromosome pixels in an image are classified. Often, the intensity distributions between different...
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