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A new method for detecting and classifying loudspeaker faults is presented in this paper. Total response of high-order harmonics groups is measured and used as defect features of loudspeaker. Based on support vector machine (SVM), we built a classification system combined with one-class SVM and Directed Acyclic Graphic SVM (DAGSVM). Comparing with K-nearest neighbor (k-NN) classifier, the accuracy...
Discretization of continuous-valued attributes is always one of the key problems in rough sets theory, a multiscale rough set model (MRSM) is developed that describes the discretization at multiple scales and analyzes the relation of classifications and certainty between scales. In view of the model's efficiency and effectiveness. an optimal scale can be acquired with self-organization, self-study...
Clustering is a hot research field in data mining. There are so many methods or algorithms designed for different type data set on which data analysis action operates. Local Agglomerative Characteristic (LAC) based Algorithm, in this paper, is presented for data clustering, which can handle clusters of different size, shapes, and densities, can work well on different distributed and natural variant...
The problem of similarity measure for time series has attracted considerable research interest. Most of the recently used algorithms utilize the Dynamic Time Warping (DTW) distance for measuring the similarity of time series, in various areas such as science, medicine, industry, and finance. DTW is a considerably more robust distance measure for time series, which allows similar shapes to match even...
Traditional algorithm of global dimensionality reduction such as PCA, MDS and Isomap, measure the relation of data by distance, this paper gives an angle measurement approach for the relation of data. Based on the theoretic analysis, a novel angle optimized global embedding (AOGE) algorithm is proposed, which measured the relation of data by the angles between the centralized samples and their orthogonal...
AdaBoost is a well-known ensemble learning algorithm that generates weak classifiers sequentially and then combines them into a strong one. Also it shows its resistance to overfitting in low noise data cases , a lot of experiments have shown that it is quite sensitive on noisy data. Several modifications to AdaBoost have been proposed to deal with noisy data. Bagging and Random forests have shown...
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