128,775 research outputs found
A Unifying View of Multiple Kernel Learning
Recent research on multiple kernel learning has lead to a number of
approaches for combining kernels in regularized risk minimization. The proposed
approaches include different formulations of objectives and varying
regularization strategies. In this paper we present a unifying general
optimization criterion for multiple kernel learning and show how existing
formulations are subsumed as special cases. We also derive the criterion's dual
representation, which is suitable for general smooth optimization algorithms.
Finally, we evaluate multiple kernel learning in this framework analytically
using a Rademacher complexity bound on the generalization error and empirically
in a set of experiments
Semi-supervised Online Multiple Kernel Learning Algorithm for Big Data
In order to improve the performance of machine learning in big data, online multiple kernel learning algorithms are proposed in this paper. First, a supervised online multiple kernel learning algorithm for big data (SOMK_bd) is proposed to reduce the computational workload during kernel modification. In SOMK_bd, the traditional kernel learning algorithm is improved and kernel integration is only carried out in the constructed kernel subset. Next, an unsupervised online multiple kernel learning algorithm for big data (UOMK_bd) is proposed. In UOMK_bd, the traditional kernel learning algorithm is improved to adapt to the online environment and data replacement strategy is used to modify the kernel function in unsupervised manner. Then, a semi-supervised online multiple kernel learning algorithm for big data (SSOMK_bd) is proposed. Based on incremental learning, SSOMK_bd makes full use of the abundant information of large scale incomplete labeled data, and uses SOMK_bd and UOMK_bd to update the current reading data. Finally, experiments are conducted on UCI data set and the results show that the proposed algorithms are effective
Neural Generalization of Multiple Kernel Learning
Multiple Kernel Learning is a conventional way to learn the kernel function
in kernel-based methods. MKL algorithms enhance the performance of kernel
methods. However, these methods have a lower complexity compared to deep
learning models and are inferior to these models in terms of recognition
accuracy. Deep learning models can learn complex functions by applying
nonlinear transformations to data through several layers. In this paper, we
show that a typical MKL algorithm can be interpreted as a one-layer neural
network with linear activation functions. By this interpretation, we propose a
Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the
conventional multiple kernel learning framework to a multi-layer neural network
with nonlinear activation functions. Our experiments on several benchmarks show
that the proposed method improves the complexity of MKL algorithms and leads to
higher recognition accuracy
- …