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In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. These deep learning approaches have been applied to image recognition, voice recognition and text processing. However, to our knowledge, the deep learning approaches have not been extensively studied for web data. In this paper, we apply deep belief networks...
This paper analyses the deficiency of SVM-RFE feature selection algorithm and puts forward a new feature selection method combined with SVM-RFE and PCA. Firstly, we get the optimal feature subset through the method of cross validation based on SVM-RFE. Then, we use the PCA method to analyse the main component about optimal feature subset and get a lower-dimension and independent data sets which are...
We present a methodology that uses Nonnegative Matrix Factorization (NMF) for feature extraction from mammogram images. These measures are used as input features for a Support Vector Machine classifier with the purpose of distinguishing tissues between normal and abnormal cases. We compared our results with another popular technique of matrix factorization called Independent Component Analysis (ICA)...
It is very important to target detecting hardware and algorithm design in the field of target tracking field. A kind of detection algorithm using SVM (support vector machine) and AdaBoost which selects representative Harr characters is proposed. SVM uses selected Harr wavelet characters as input data in training and classifying procedure. In order to accelerate SVM classify and detect speed, the cascade...
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