One of the main tasks in kernel methods is selection adequate mappings into higher dimension order to improve class classification. However, this tends be time consuming, and it may not finish with best separation between classes. Therefore, there a need for better that are able extract distance from data. This work presents novel approach learning such by using locally stationary kernels, spec...

A novel descriptor for pattern recognition is proposed by using dual-tree complex wavelet features and SVM. The approximate shiftinvariant property of the dual-tree complex wavelet and its good directional selectivity in 2D make it a very appealing choice for pattern recognition. Recently, SVM has been shown to be very successful in pattern recognition. By combining these two tools we find that...

This paper studies construction of quantum Gaussian processes based on ordinary Gaussian processes through their reproducing kernel Hilbert spaces, and investigate the relationship between the stochastic properties of the quantum Gaussian processes and the base Gaussian processes. In particular, we construct quantum Brownian bridges and quantum Ornstein-Uhlenbeck processes. Non-commutative stoc...

The ability to automatically discover patterns and perform extrapolation is an essential quality of intelligent systems. Kernel methods, such as Gaussian processes, have great potential for pattern extrapolation, since the kernel flexibly and interpretably controls the generalisation properties of these methods. However, automatically extrapolating large scale multidimensional patterns is in ge...