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The framework consisting of a pixel-wise classification followed by a Markov random field has been very successful for spatial-spectral hyperspectral classification. While training such frameworks, the classifier and the Markov random field are generally tuned greedily one after another. However, better results could be obtained by tuning both of the components simultaneously with the objective of...
Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the...
This paper presents a novel approach for automatic optimisation of reconfigurable design parameters based on knowledge transfer. The key idea is to make use of insights derived from optimising related designs to benefit future optimisations. We show how to use designs targeting one device to speed up optimisation of another device. The proposed approach is evaluated based on various applications including...
Aiding design and test optimization of analog circuits requires accurate models that can reliably capture complex dependencies of circuit performances on essential circuit and device parameters, and test signatures. We present a novel Bayesian learning technique, namely relevance vector and feature machine (RVFM), for characterizing analog circuits with sparse statistical regression models. RVFM not...
Many algorithms used for the analysis of physiological signals include hyper-parameters that must be selected by the investigator. The ultimate choice of these parameter values can have a dramatic impact on the performance of the approach. Addressing this issue often requires investigators to manually tune parameters for their particular data-set. In this study, we illustrate the importance of global...
The parameters plays an important role to the performance of support vector regression(SVR). In order to solve the problem of the Parameter optimization for SVR, first, we transform the problem of Parameter optimization into a problem of nonlinear system state estimation, then, we propose a novel algorithm based on Dual Recursive Variational Bayesian Adaptive Square-Cubature Kalman Filter (DRVB-ASCKF),...
In this work, we investigate into the abstaining classification of binary support vector machines (SVMs) based on mutual information (MI). We obtain the reject rule by maximizing the MI between the true labels and the predicted labels, which is a post-processing method. The gradient and Hessian matrix of MI are derived explicitly so that Newton method is used for the optimization which converges very...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
Classical relevance vector machine is sensitive to outliers during training and has weak robustness. In order to solve this problem, a novel robust relevance vector machine is presented in this paper. The key idea of the proposed method is to introduce individual noise variance coefficient for each training sample. In the process of model training, the noise variance coefficients of outliers gradually...
In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method...
Nonparametric kernel methods are widely used and proven to be successful in many statistical learning problems. Well--known examples include the kernel density estimate (KDE) for density estimation and the support vector machine (SVM) for classification. We propose a kernel classifier that optimizes the L2 or integrated squared error (ISE) of a “difference of densities.” We focus on the Gaussian kernel,...
As a popular and competent kernel function in kernel based machine learning techniques, conventional Gaussian kernel has unified kernel width with each of basis functions, which make impliedly a basic assumption: the response signal represents below certain frequency and the noise represents above such certain frequency. However, in many case, this assumption does not hold. To overcome this limitation,...
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