Multi-empirical kernel classifier based on Universum learning

Abstract

The present invention provides a multi-empirical kernel classifier based on Universum learning. Sample data is subjected to multi-empirical kernel mapping, and Universum sample data is generated in the sample space in each mapping. The present invention designs a Universum sample generation mode IMU (Imbalanced Modified Universum) which is independent of the model and can be utilized in other Universum learning. Imbalance of samples is introduced in the IMU to better solve the imbalance problem. A regularization item is designed according to the Universum sample, the regularization item is introduced into a multi-empirical kernel model to form a multi-empirical kernel classifier MUEKL (Multiple Universum-based Empirical Kernel Learning) based on the Universum learning. According to the generated target function, unknown parameters are subjected to optimization solution. Different from known multi-kernel learning, the multi-empirical kernel classifier combines the multi-empirical kernellearning and the Universum learning to provide an MUEKL algorithm; the expression on the balance data set is excellent through combination of the kernel learning and the Universum learning, and the imbalance problem is solved; and moreover, the present invention further provides a Universum data generation mode IMU which has a wide application.

Description

technical field [0001] The invention relates to the field of pattern recognition technology, and is a multi-experience kernel learning mechanism based on Universun learning. Background technique [0002] Algorithms based on kernels have been used in many applications and have achieved good results. The kernel mapping method is an effective method for solving nonlinear problems. The kernel mapping method refers to mapping the input space X to a new feature space Γ through a kernel function Φ, and the kernel function is defined as follows Φ:X→Γ. Divided by mapping form, kernel mapping can be divided into implicit kernel mapping Implicit Kernel Mapping (IKM)Φ i And Empirical Kernel Mapping(EKM)Φ e . Stealth kernel mapping is achieved through the kernel function k(x i ,x j ) = Φ i (x i )·Φ i (x j ) is mapped, where Φ i The specific form of does not need to be given. Stealth kernel mapping can map the input space to an infinite space, because the space after stealth k...

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However, Universum learning and multi-experien

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