This paper proposes a feature selection based classification method that can be applied to better accuracy in the Digital Radiographs (DR) for the identification of Inferior Alveolar Nerve Injury (IANI). Different conventional features based on the shape and EZW (Embedded Zero tree Wavelet) based texture features are extracted using different feature extraction techniques. Then, aggregate votin...

This thesis examines a support vector machine approach for determining consumer credit. The support vector machine using a radial basis function (RBF) kernel is compared to a previous implementation of a decision tree machine learning model. The dataset used for evaluation was provided by a large bank and includes relevant consumer-level data, including transactions and credit-bureau data. The ...

A new direction in machine learning area has emerged from Vapnik’s theory in support vectors machine and its applications on pattern recognition. In this paper, we propose a new SVM kernel family (KMOD) with distinctive properties that allow better discrimination in the feature space. The experiments that we carry out show its effectiveness on synthetic and large-scale data. We found KMOD behav...

entropy as a measure of uncertainty was used to represent the results of the wavelet technique in electrochemical noise analysis. the experimental signals were obtained by recording the electrochemical potential and current noise of 7075 aluminum alloy in 3.5% nacl solution. the electrochemical potential and current noise were decomposed into 16 levels using daubechies wavelets. wavelet output ...

To enhance the accuracy of protein–protein interaction function prediction, a 2-order graphic neighbor information feature extraction method based on undirected simple graph is proposed in this paper , which extends the 1-order graphic neighbor featureextraction method. And the chi-square test statistical method is also involved in feature combination. To demonstrate the effectiveness of our 2-...

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...

Gaussian Process (GP) has gained much attention in cosmology due to its ability reconstruct cosmological data a model-independent manner. In this study, we compare two methods for GP kernel selection: Approximate Bayesian Computation (ABC) Rejection and nested sampling. We analyze three types of data: cosmic Chronometer (CC), Type Ia Supernovae (SNIa), Gamma Ray Burst (GRB), using five function...

In order to capture an eye image of high quality in a gaze-tracking camera, an auto-focusing mechanism is used, which requires accurate focus assessment. Although there has been previous research on focus assessment in the spatial or wavelet domains, there are few previous studies that combine all of the methods of spatial and wavelet domains. Since all of the previous focus assessments in the ...

The local radial basis function (RBF) interpolation method enables very large-scale data sets to be handled efficiently, overcoming the drawbacks of global interpolation which produces highly ill-conditioned linear systems. Whereas there have been intensive studies on the accuracy of global RBF interpolation, the error analysis of local RBF interpolation is much less investigated. In this regar...

In this paper we introduce a novel graph classification algorithm and demonstrate its efficacy in drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to create features capturing graph local topology. We design a novel graph kernel function to utilize the created feature to build predictive models for chemicals. We call the new graph ker...