Search results for "Radial basis function"
showing 10 items of 61 documents
Model selection based product kernel learning for regression on graphs
2013
The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the…
A structural cluster kernel for learning on graphs
2012
In recent years, graph kernels have received considerable interest within the machine learning and data mining community. Here, we introduce a novel approach enabling kernel methods to utilize additional information hidden in the structural neighborhood of the graphs under consideration. Our novel structural cluster kernel (SCK) incorporates similarities induced by a structural clustering algorithm to improve state-of-the-art graph kernels. The approach taken is based on the idea that graph similarity can not only be described by the similarity between the graphs themselves, but also by the similarity they possess with respect to their structural neighborhood. We applied our novel kernel in…
Numerical Solution of Foodstuff Freezing Problems Using Radial Basis Functions
2013
This work presents a novel numerical approach for the solution of time dependent non-linear freezing processes in terms of radial basis function Hermite approach. The proposed scheme is applied to a mashed potato sample during its freezing; evaluation of time evolution of the temperature profile at the core of the sample is carried out. Food thermal properties are highly dependent on temperature and the mathematical problem becomes highly non-linear and therefore particularly difficult to solve. Incorporating a Kirchhoff transformation significantly reduces the non-linearity. The robustness of the scheme is tested by comparison with experimental results available in literature.
A Novel System for Multi-level Crohn’s Disease Classification and Grading Based on a Multiclass Support Vector Machine
2020
Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI…
Learning with the kernel signal to noise ratio
2012
This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extract…
Distributed learning automata for solving a classification task
2016
In this paper, we propose a novel classifier in two-dimensional feature spaces based on the theory of Learning Automata (LA). The essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a grid system. To each node in the gird we attach an LA, whose actions are the choice of the edges forming the separator. The walk is self-enclosing, i.e, a new random walk is started whenever the walker returns to starting node forming a closed classification path yielding a many edged polygon. In our approach, the different LA attached at the different nodes search for a polygon that best encircles and separates each class. Based on the obtained polygon…
An Introduction to Kernel Methods
2009
Machine learning has experienced a great advance in the eighties and nineties due to the active research in artificial neural networks and adaptive systems. These tools have demonstrated good results in many real applications, since neither a priori knowledge about the distribution of the available data nor the relationships among the independent variables should be necessarily assumed. Overfitting due to reduced training data sets is controlled by means of a regularized functional which minimizes the complexity of the machine. Working with high dimensional input spaces is no longer a problem thanks to the use of kernel methods. Such methods also provide us with new ways to interpret the cl…
Learning to Approach a Moving Ball with a Simulated Two-Wheeled Robot
2006
We show how a two-wheeled robot can learn to approach a moving ball using Reinforcement Learning. The robot is controlled by setting the velocities of its two wheels. It has to reach the ball under certain conditions to be able to kick it towards a given target. In order to kick, the ball has to be in front of the robot. The robot also has to reach the ball at a certain angle in relation to the target, because the ball is always kicked in the direction from the center of the robot to the ball. The robot learns which velocity differences should be applied to the wheels: one of the wheels is set to the maximum velocity, the other one according to this difference. We apply a REINFORCE algorith…
Non-parametric spectrum cartography using adaptive radial basis functions
2017
This paper presents a framework for spectrum cartography based on the use of adaptive Gaussian radial basis functions (RBF) centered around a specific number of centroid locations, which are determined, jointly with the other RBF parameters, by the available measurement values at given sensor locations in a specific geographical area. The spectrum map is constructed non-parametrically as no prior knowledge about the transmitters is assumed. The received signal power at each location (over a given bandwidth and time period) is estimated as a weighted contribution from different RBF, in such a way that the both RBF parameters and the weights are jointly optimized using an alternating minimiza…
On the reconstruction of discontinuous functions using multiquadric RBF–WENO local interpolation techniques
2020
Abstract We discuss several approaches involving the reconstruction of discontinuous one-dimensional functions using parameter-dependent multiquadric radial basis function (MQ-RBF) local interpolants combined with weighted essentially non-oscillatory (WENO) techniques, both in the computation of the locally optimized shape parameter and in the combination of RBF interpolants. We examine the accuracy of the proposed reconstruction techniques in smooth regions and their ability to avoid Gibbs phenomena close to discontinuities. In this paper, we propose a true MQ-RBF–WENO method that does not revert to the classical polynomial WENO approximation near discontinuities, as opposed to what was pr…