To improve the performance of support vector machines (SVM), a hybrid kernel is constructed from the existing common kernels, and the hyper-parameters are optimized by using a quasi-Newton method.
为了提高支持向量机(SVM)的识别性能，提出了在常用内核的基础上构造一个组合内核函数，然后用拟牛顿算法对其超参数进行优化的方法。
Due to the non-convex of the prior function and hyper-parameters, we use the dynamic posterior simulation rather than the general optimization methods to get reconstruction image.
由于采用的先验函数是非凸的并包含超验参数，一般的优化方法难以处理，采用动态后验模拟的方法可以很好地解决这些问题。
The full state hybrid projective synchronization of chaotic and hyper-chaotic systems with fully unknown parameters is further investigated.
对于不同维数的两个混沌系统，提出了降阶全状态混合投影同步、增阶全状态混合投影同步的概念。
The full state hybrid projective synchronization of chaotic and hyper-chaotic systems with fully unknown parameters is further investigated.
对于不同维数的两个混沌系统，提出了降阶全状态混合投影同步、增阶全状态混合投影同步的概念。
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