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The prediction of molecule's properties through Quantitative Structure Activity (resp. Property) Relationships are two active research fields named QSAR and QSPR. Within these frameworks Graph kernels allow to combine a natural encoding of a molecule by a graph with classical statistical tools such as SVM or kernel ridge regression. Unfortunately some molecules encoded by a same graph and differing...
Bipolar magnetic regions (BMRs) are the corner-stone of solar variability. They are tracers of the large-scale magnetic processes that give rise to the solar cycle, shapers of the solar corona, building blocks of the large-scale solar magnetic field, and significant contributors to the free-energetic budget that gives rise to flares and coronal mass ejections. Surprisingly, no homogeneous catalog...
Support Vector Machine (SVM) is one of the most popular machine learning algorithms for pattern recognition of a specific dataset. The percentage of accuracy from a defined SVM model greatly depends on the selection of appropriate attributes for SVM model. But the most effective attributes selection for SVM algorithm is one of the most difficult tasks for any kind of data classification. A mathematical...
This work presents a dimensionality reduction (DR) framework that enables users to perform either the selection or mixture of DR methods by means of an interactive model, here named Geo-Desic approach. Such a model consists of linear combination of kernel-based representations of DR methods, wherein the corresponding coefficients are related to coordinated latitude and longitude inside of the world...
Tongue diagnosis is one of the main components of traditional Chinese medicine (TCM). Developing an objective and quantitative recognition model is very importantly and useful in the modernization of TCM. Currently, major problems in digital diagnoses of tongue images are extracting suitable features and building a high-performance classifier. To address these two issues, we present a robust approach...
Multivariate methods of pattern recognition, classification and discriminant analysis have been found most useful in many types of chemical and biological problems. Predicting the biological activity of molecules from their chemical structures is a principal problem in drug discovery. Pattern recognition has gained attention as methods covering this need. In the present study classification models...
A new image segmentation method is proposed in this paper for improving the effect of the image segmentation. First, an original image is nonlinear mapped into a higher dimension kernel space, and the data are better separated under the kernel space comparing with that under the original image space, then, the number of categories of the image is determined by analyzing the image histogram using gauss...
We summarize the history and state of the art in Convolutional Neural Networks (CNNs), which constitute a significant advancement in pattern recognition. As a demonstration of capability, we address the problem of automatic aircraft identification during refueling approach. In this paper we describe the history of CNN development and provide a high level overview of the state of the art and a summary...
In this paper, an asymmetric kernel is proposed for extracting sparse features from two-dimensional visual face images for identity recognition. Essentially, the kernel consists of an inner product of two vectors where one of them has been raised to power terms element-wise. The impact of such a power term is suppression of less influential features where only relevant ones are used for estimation...
SVM classifiers with Half Against Half (HAH) architecture are reported to be the fastest classifier amongst other SVM classification architectures reported in literature. An attempt is made to enhance the speed of HAH SVM classifier and is named as Fast HAH (F-HAH) classifier. The performance of proposed F-HAH classifier is evaluated using speaker dependent and multi-speaker dependent isolated digits...
This paper presents a model of Pulse-Coupled Neural Network (PCNN) for multispectral image segmentation. Its application for license plate recognition (LPR) is considered; this consists of three processing steps. First step extracts the license plate coordinates from the original image; second step is the PCNN-based segmentation method to obtain a binary image containing only the characters of the...
A robust model is sought for the identification of electroencephalographic (EEG) signals including movements of three distinct parts of the user's arm, namely hand, elbow and shoulder. This study investigates the classification performances of the same upper limb motor movements using various kernel functions of the support vector machine (SVM). Polynomial, linear and radial basis (RBF) functions...
Recently, One Class Support Vector Machines (OCSVM) have been the subject of much research. This paper introduces a novel pattern classification approach for Broken Rotor Bar (BRB) detection in Induction Motors (IM), which combines Stationary Wavelet Packet Transform (SWPT) and OCSVM. Among all the kernels available to OCSVM, wavelet kernels are tuned to improve accuracy and detection time of fault...
Sclera blood vessels have been investigated recently as an efficient biometric trait. Capturing this part of the eye with a normal camera using visible-wavelength images rather than near-infrared images has provoked research interest. However, processing noisy sclera images captured at-a-distance and on-the-move has not been extensively investigated. Therefore in this paper, we propose a new method...
Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM, depends on different parameters such as: penalty factor, C, and the kernel factor, o. Also choosing an appropriate kernel function can improve the Recognition Score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset...
Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting...
Support Vector Machines are widely accepted in the field of pattern recognition because of their superiority in performing supervised classification. It is known that all kernel parameters may be used for classification more-or-less precisely (giving rise to vagueness) and also for the same classification problem, there are a number of kernel parameters which give the best accuracy (giving rise to...
To recognize different bearing fault patterns under different operating conditions, a novel multi-team competitive optimization (MTCO) algorithm is proposed. The algorithm is inspired by the competitive behaviors among multiple teams. In the structure, it consists of a three-level optimization organization structure, so that the more potential optimal areas can be searched. Meanwhile, some new strategies...
Dimensionality reduction (DR) methods represent a suitable alternative to visualizing data. Nonetheless, most of them still lack the properties of interactivity and controllability. In this work, we propose a data visualization interface that allows for user interaction within an interactive framework. Specifically, our interface is based on a mathematic geometric model, which combines DR methods...
In this paper, a novel technique for human daily motion analysis and recognition is proposed. The technique is based on the use of inertial sensors, and integrates a longest common subsequences (LCSS) algorithm as the kernel function for support vector machines (SVM), which measures the similarity of human daily motion time-series. In our system, we use the wearable motion capture system to obtain...
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