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This paper presents a budgetary learning algorithm for online multiclass classification. Based on the multiclass passive-aggressive learning with kernels, we introduce a dual perspective that gives rise to the proposed budgetary algorithm. Basically, the proposed algorithm limits the amount of data in use and fully exploits the available data on hand through optimization. The algorithm has both constant...
The paper presents nonlinear modeling study of belt conveyor system using the least square support vector machine (LS-SVM). Belt conveyor is a nonlinear, severe disturbance and time-varying system. So far, most of the existing models are based on mechanism laws, which are very useful for belt conveyor design. However, they are too complicated to be applied to control system design. To facilitate a...
This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and...
Speech recognition systems are usually trained using tremendous transcribed utterances, and training data preparation is intensively time-consuming and costly. Aiming at reducing the number of training examples to be labeled, active learning is used in acoustic modeling of speech recognition, this learning scheme iteratively inspects the unlabeled samples, selects the most informative samples corresponding...
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