Zhiping Dan

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Keywords: Self-training Semi-supervised classification Semi-supervised clustering Fuzzy c-means Support vector machine a b s t r a c t Semi-supervised classification has become an active topic recently and a number of algorithms, such as Self-training, have been proposed to improve the performance of supervised classification using unlabeled data. In this(More)
In this paper, a novely method for speaker system is proposed. The least square support vector machine (LS-SVM) based on the quadratic equality constraints is analyzed firstly. A speaker recognition system is then designed based on LS-SVM. The Mel frequency ceptral coefficients (MFCCs) are adopted as the speaker speech feature parameters in the system. The(More)
As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) is based on a discounted reward reinforcement learning algorithm, bucket-brigade algorithm, which optimizes the discounted total reward received by an agent but is not suitable for all multi-step problems, especially large-size ones. There are some undiscounted(More)
Aim at magnetic resonance imaging (MRI) inherent bias field, a new method to estimate image bias field based on diffusion was proposed, which was based on an intuitive assumption that the true bias field can be evolved and approximated by image surface. To constrain this evolution, an energy function was designed based on two diffusion constraints. One was(More)
Ship target recognition in infrared image remains a difficult problem, due to the projection or silhouette of a three-dimensional ship target being variable in shape, orientation and scale to make its recognizability unstable. In this paper, a transductive transfer learning framework is proposed to solve the problem. Hu moments is firstly extracted as(More)
Face recognition has attracted considerable concerns in recent years. In practical applications, there are generally a small amount of labeled face images and a lot of unlabeled ones can be available. In this paper, we introduce a semi-supervised face recognition method where semi-supervised LDA (SDA) and Affinity Propagation (AP) are integrated into(More)
A locally adaptive shrinkage Bayesian estimate for medical ultrasonography denoising is proposed by exploiting the correlation among image sparse coding. The Laplacian distribution is used to model the coding coefficients. The paper deduces the MAP estimate formula and adaptive threshold. Simulation experiments are carried out to show the effectiveness of(More)
A locally adaptive Bayesian estimate for image denoising is proposed by exploiting the correlation among image shear let coefficients in a sub-band. The Laplacian distribution can model a wide range of process, from heavy-tailed to less heavy-tailed processes. This paper deduces Laplacian prior distribution based the MAP estimate formula and sub-band(More)
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