This paper reviews relatively new developed techniques for machine health prognostics system. The prognostics assessment of machines is an important consideration for determining the remaining useful life (RUL) of machine components and prediction of future state of machines. The developed system has employed several approaches of machine health prognostics strategy such as data-driven, physica...

When machine learning is applied in safety-critical or otherwise sensitive areas, the analysis of feature relevance can be an important tool to keep the size of models small, and thus easier to understand, and to analyze how different features impact the behavior of the model. In the presence of correlated features, feature relevances and the solution to the minimal-optimal feature selection pr...

Brushless permanent magnet surface inset machines are interested in industrial applications due to their high efficiency and power density. Magnet segmentation is a common technique in order to mitigate cogging torque and electromagnetic torque components in these machines. An accurate computation of magnetic vector potential is necessary in order to compute cogging torque, electromagnetic torq...

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernelbased algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, k...

Gaussian Processes (GPs) have state of the art performance in regression. In GPs, all the basis functions are required for prediction; hence its test speed is slower than other learning algorithms such as support vector machines (SVMs), relevance vector machine (RVM), adaptive sparseness (AS), etc. To overcome this limitation, we present a backward elimination algorithm, called GPs-BE that recu...

bivariate features, obtained from multichannel electroencephalogram (eeg) recordings, quantify the relation between different brain regions. studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. a new bivariate approach using univariate features is proposed here. differences and ratios ...

The relevance vector machine (RVM) (Tipping, 2001) encapsulates a sparse probabilistic model for machine learning tasks. Like support vector machines, use of the kernel trick allows modelling in high dimensional feature spaces to be achieved at low computational cost. However, sparsity is controlled not just by the automatic relevance determination (ARD) prior but also by the choice of basis fu...

Consumer health information search (CHIS) is a forum of information retrieval that has organized two tasks to be performed. The first task includes the identification that whether a given query is relevant or irrelevant to the sentences available in the document. The second tasks talks about finding the nature of support of a sentence in the document to the query. Task 1 i.e identification of r...

This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...

In this work, we developed classifiers to distinguish between four ovarian tumor types using Bayesian least squares support vector machines (LS-SVMs) and kernel logistic regression. Input selection using rank-one updates for LS-SVMs performed better than automatic relevance determination. Evaluation on an independent test set showed good performance of the classifiers to distinguish between all...