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The purpose of data mining is to explore, find and hence analyze relevant data from a massive data source using various technical means. This paper introduces the development of data mining to date, its functions, tasks and algorithms, as well as the process of data mining. The application and problems of data mining are also presented and finally the potential future development of data mining technology...
Existing extended one-versus-rest multi-label support vector machine (OVR-ESVM) adopting non-linear kernel is seriously restricted by excessive training time when it is applied to large-scale data set. In order to overcome this problem, we improve the OVR-ESVM by introducing the principle of approximate extreme points and new approximate ranking loss to construct a novel extended OVR-ESVM using approximate...
Epilepsy is increasingly occurred disease in the modern world, and the use of automatic detection technology of epileptic Electroencephalogram (EEG) signals is more and more important. In this essay, a novel approach named CC-ELM of automatic epileptic EEG signals detection is proposed. Unlike traditional dimension reducing methods of most current automatic detection, the proposed approach adopts...
Classification of musical instrument is resulted from musicology. Thus, automatic identification of instrument families not only benefits the study in musicology, but also worth of attention in MIR. Based on a database consists of 2177 clips from Chinese and Western instrumental music, the experiments provided in this paper evaluate the ability of automatic Chinese and Western instrument identification...
Instrumental music is often classified or retrieved in terms of instruments played in it. With a large database consists of Chinese traditional music and western classical music, this paper extracted several features to automatically classify Chinese and western instruments by SVM classifier, and analyzed the classification results.
In many applications, one class of data is presented by a large number of examples while the other only by a few. For instance, in our previous works on identification of peer-to-peer (P2P) Internet traffics, we observed that only about 30% of examples can be labeled as ldquoP2Prdquo using a port-based heuristic rule, and even fewer examples can be labeled in the future as more and more P2P applications...
The training of the adaboost algorithm for face detection is time costly; it often needs days or weeks in the previous system. In this paper, we describe efficient optimization techniques and implement skills to reduce the training time. First we use some preprocessing technique to reduce the candidate features size to ten percent of the original, and then we use some implement skills to further reduce...
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...
Particle swarm optimization was applied in BP neural network training. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in realities. Meanwhile, PSO-BP neural network is applied into classification of fabric defect. The method of orthogonal wavelet transform was used to decompose monolayer from fabric image. And the sub-images of...
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