Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

“…SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.…”
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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

“…SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.…”
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Explainable machine learning and feature engineering applied to nanoindentation data by C.O.W. Trost, S. Žák, S. Schaffer, L. Walch, J. Zitz, T. Klünsner, H. Leitner, L. Exl, M.J. Cordill

“…Features based on dimensional analysis initially aimed to solve the inverse nanoindentation problem were adopted to describe the load–displacement curves. …”
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Clinic morphological features of Goodpasture’s syndrome manifested with respiratory disorders by Yu. M. Mostovoy, A. V. Demchuk, T. V. Konstantynovych, N. O. Pentiuk, L. V. Blazhevska

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Transportation scene recognition based on high level feature representation by Wenhua LIU, Yidong LI, Tao WANG, Jun WU, Yi JIN

“…With the development of intelligent transportation,it has become an urgent problem to quickly and accurately recognize complex traffic scene.In recent years,a large number of scene recognition methods have been proposed to improve the effectiveness of traffic scene recognition,however,most of these algorithms cannot extract the semantic characteristics of the concept of vision,leading to the low recognition accuracy in traffic scenes.Therefore,a novel traffic scene recognition algorithm which extracts the high-level semantic and structural information for improving the accuracy was proposed.A system to discover semantically meaningful descriptions of the scene classes to reduce the “semantic gap” between the high level and the low-level feature representation was built.Then,the multi-label network was trained by minimizing loss function (namely,element-wise logistic loss) to obtain the high-level semantic representation of traffic scene images.Finally,experiments on four large-scale scene recognition datasets show that the proposed algorithm considerably outperforms other state-of-the-art methods.…”
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Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation by ZHANG Xue-qin, LIN Ke-zheng, LI Ao

“…In order to solve the problem that the face image is not rich in features extracted under complex lighting environments，which leads to a low recognition rate，a local pattern feature extraction and recognition algorithm based on sparse representation is proposed. …”
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Improving Image Embeddings With Colour Features in Indoor Scene Geolocation by Opeyemi Bamigbade, Mark Scanlon, John Sheppard

“…Embeddings remain the best way to represent image features, but do not always capture all latent information. …”
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Impact of Lexical Features on Answer Detection Model in Discussion Forums by Atif Khan, Muhammad Adnan Gul, Abdullah Alharbi, M. Irfan Uddin, Shaukat Ali, Bader Alouffi

“…Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. …”
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Effect of oceanic turbulence on the statistical features of electromagnetic optical beam by Hemant Kumar Singh, Vikram Singh Bhandari, Bhaskar Kanseri

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Specific features of team kinds of sports sportsmen’s individual characteristics by V.N. Liashenko, V.N. Tumanova, E. V. Hatsko, Y.N. Korzh

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The maximum residual block Kaczmarz algorithm based on feature selection by Ran-Ran Li, Hao Liu

“…However, with the increase of the size of the coefficient matrix, the time required for the partitioning process will increase significantly. To address this problem, we considered selecting features from the columns of the matrix $ A $ to obtain a low-rank matrix $ \tilde A \in {\mathbb{R}^ {m \times d}} \left(d \ll n \right) $. …”
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Generations of Odessa historians (1920–1941): Identification, composition, and features by V.V. Levchenko

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RESEARCH FEATURES AND EFFICIENCY OF ANTIHYPERTENSIVE THERAPY IN PATIENTS WITH METABOLIC DISORDERS by E. I. Polozova, E. I. Polozova, E. V. Puzanova, A. A. Sesekina, I. S. Ovsyannikova, E. N. Domina, I. A. Surkova

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RISKS IN STRATEGIC PLANNING AND IN THE BUDGETARY SPHERE: FEATURES OF LEGAL REGULATION by LOGVENCHEVA A.O.

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Hybrid feature learning framework for the classification of encrypted network traffic by S. Ramraj, G. Usha

“…Additionally, various feature learning frameworks based on deep learning, such as DNN, Autoencoder and PCA, are compared. …”
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SIFT Feature-Based Video Camera Boundary Detection Algorithm by Lingqiang Kong

“…Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. …”
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Features of Hybrid Identity of the Second/Third Generation of European Muslims by Larisa A. Andreeva

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Features of phenotyping patients with heart failure with preserved ejection fraction by E. K. Serezhina, A. G. Obrezan

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Features of Family Environment of a Child in Post-Industrial Society by A. D. Andreyeva

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Spatial Circular Granulation Method Based on Multimodal Finger Feature by Jinfeng Yang, Zhen Zhong, Guimin Jia, Yanan Li

“…How to reliably and effectively fuse the multimodal finger features together, however, has still been a challenging problem in practice. …”
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