It has been shown that Support Vector Machine theory optimizes a smoothness functional hypothesis through kernel application. We present KMOD, a two-parameter SVM kernel with distinctive properties of good discrimination between patterns while preserving the data neighborhood information. In classi£cation problems, the experiments we carried out on the Breast Cancer benchmark produced better pe...

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter spac...

Background: Developing an accurate computer-aided diagnosis (CAD) system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (D...

Background: Developing an accurate computer-aided diagnosis (CAD) system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (D...

Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establish learning rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF kernels are used and the desired expectile is smooth in a Besov sense. As a special case, ...

● Architecture allows us to induce rules of predefined structure ● We can, for instance, incorporate the inductive bias of a transitivity relationship in the knowledge base θ1(X,Y) :θ2(X,Z), θ3(Z,Y). ● θi are vector representations for unknown predicates ● They can be learned like all other vector representations ● They can be decoded at test time by finding the closest known relation using the...

A two-layer neural network is developed in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted logistic function with the mean of the partial input-weight similarities. The model is capable of dealing directly with variables of potentially different nature (continuous, ordinal, categorical); t...

This paper describes the design of multi-category support vector machines (SVMs) for classification of bags. To train and test the SVMs a collection of 120 images of different types of bags were used (backpacks, small shoulder bags, plastic flexible bags, and small briefcases). Tests were conducted to establish the best polynomial and Gaussian RBF (radial basis function) kernels. As it is well ...

This chapter introduces a novel Levenberg-Marquardt like second-order algorithm for tuning the Parzen window σ in a Radial Basis Function (Gaussian) kernel. In this case, each attribute has its own sigma parameter associated with it. The values of the optimized σ are then used as a gauge for variable selection. In this study, the Kernel Partial Least Squares (K-PLS) model is applied to several ...

We explore ways of applying a prior on the covariance matrix of a Gaussian Process (GP) in order to increase its expressive power. We show that two well-known covariance priors, Wishart Process and Inverse Wishart Process, boil down to a two-layer feed-forward network of GPs with a particular kernel function on the neuron at the output layer. Both of these models perform supervised manifold lea...