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Several approaches in machine learning exist to cover this span of imitative behaviors.
Regarding the joint decision-making, there are many approaches in machine learning [25] that can be useful.
Extreme learning machine (ELM) represents one of the recent successful approaches in machine learning, particularly for performing pattern classification.
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The developed model is based on the multiple saliency model and is formulated using winding function approach in machine variables.
Reinforcement learning (RL) is an effective approach in machine learning that has been applied for designing adaptive traffic signal controllers.
These findings revealed that the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.
The signature-based malware detection has two main methods for applying malware detection approach in machine learning methods including assembly features and binary features.
What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.
DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures.
The one-class approach in machine learning has been receiving more attention particularly for solving problems where the negative class is not well defined [ 22- 25]; moreover, the one class approach has been successfully applied in various fields including text mining [ 26], functional Magnetic Resonance Imaging (fMRI) [ 27] and signature verification [ 28].
At the other extreme are algorithmic approaches found in machine learning and emphasized in this paper.
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