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Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve...
Nowadays, humans can play an important role in control of robots. Some researches have used signals that coming directly from humans for control interfaces. In this paper, electromyogram (EMG) signals from the muscles of the human's upper limb are used as the control interface between the user and a robot arm. A Multi-Layer Perceptron (MLP) is trained by additional unsupervised pre-training to decode...
Representation learning is a fast growing approach in machine learning that aims to improve the quality of the input data, instead of insisting on designing complex subsequent learning algorithms. In this paper, we propose to use Denoising AutoEncoders (DAEs), as one of the most effective representation learning methods, in Clustering-based Classification (CC). CC is a multi-class classification solution...
Multi-class classification is a challenging problem in pattern recognition. Clustering-based Classification (CC) is one of the most effective classification methods that first divides data into several clusters, each cluster then being described by a One-Class Classifier (OCC). Scalability and accuracy are two key advantages of this clustering-enhanced approach. In continuation of this strategy, in...
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