VIOLET: Vectorized Invariance Optimization for Language Embeddings Using Twins by Mikhail E. Ram, G. Manju

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Noospheric vector of management applied to the system of higher professional education by Yulia A. Mishina, Anna V. Veriga

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Application of Deep Support Vector Machine in Gear Fault Diagnosis by Lei Yu, Sen Chen, Rui Zhang, Ke Li, Lei Su

“…Secondly, the multi-layer support vector is constructed. The SVM is used to train the training sample on the input layer, and it learns the shallow features of the data. …”
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Sentiment Analysis using Support Vector Machine and Random Forest by Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Bin Mohd Su'ud

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Vector Effects of Dissipative Soliton in All-Fiber MOPA System by Xiang-Yue Li, Shi-Lin Liu, Meng Liu, Ai-Ping Luo, Zhi-Chao Luo, Wen-Cheng Xu

“…We demonstrated that, although the intensity of the dissipative soliton seed source is uniform, the intensity of the output pulse train from the MOPA will show fluctuations if the soliton polarization state is not locked, i.e., polarization-rotating vector soliton. …”
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A New Support Vector Machine Based on Convolution Product by Wei-Chang Yeh, Yunzhi Jiang, Shi-Yi Tan, Chih-Yen Yeh

“…The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. …”
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Prediction of Customer Switching Using Support Vector Machine Method by Abu Tholib, Selfia Hafidatus Sholeha, Qurrotu Aini

“…The Neural Network model can be trained with better patterns to detect data and achieve high accuracy. …”
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GeoFAN: Point Pattern Recognition in Spatial Vector Data by Zhuoyi Yang, Zeyi Li, Haitao Zhang, Wei Zhang, Yanwei Wang, Yihang Huang

“…The recognition of point patterns in spatial vector data has important applications in geographic mapping and formation recognition. …”
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Active Learning Music Genre Classification Based on Support Vector Machine by Guanghui Deng, Young Chun Ko

“…The improved SVM (support vector machine) offers an active training method that provides users with the most informative sample through multiple iterations and adds it to the training package, which can significantly reduce the cost of manually labeling samples. …”
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Neural network model for dependency parsing incorporating global vector feature by Hengjun WANG, Nianwen SI, Yulong SONG, Yidong SHAN

“…LSTM and piecewise CNN were utilized to extract word vector features and global vector features,respectively.Then the two features were input to feed forward network for training.In model training,the probabilistic training method was adopted.Compared with the original dependency paring model,the proposed model focused more on global features,and used all potential dependency trees to update model parameters.Experiments on Chinese Penn Treebank 5 (CTB5) dataset show that,compared with the parsing model using LSTM or CNN only,the proposed model not only remains the relatively low model complexity,but also achieves higher accuracies.…”
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Localization algorithm based on support vector regression for wirless sensor networks by WEI Ye-hua1, LI Ren-fa2, LUO Juan2, FU Bin2

“…Aiming at these drawbacks, a semi-centralized localization algorithm based on support vector regression was presented. The base node collected the position of nodes and all connectivity information between anchor nodes as training samples to run the training procedure with support vector regression method. …”
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Face Mask Wearing Detection Using Support Vector Machine (SVM) by Muhammad Nur Yasir Utomo, Fajrin Violita

“…Thus, this research proposes a face mask-wearing detection using a soft-margin Support Vector Machine (SVM). There are three main stages: feature selection and preprocessing, model training, and evaluation. …”
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Psychological and Pedagogical Bases for Determining the Future of Vocational Education: Vectors of Development by Yu. P. Zinchenko, E. M. Dorozhkin, E. F. Zeer

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Service recommendation method based on context-embedded support vector machine by Chenyang ZHAO, Junling WANG

“…Combined with contexts and SVM,a service recommendation method based on context-embedded support vector machine (SRM-CESVM) was proposed.Firstly,according to the different contexts,the user rating matrix was modified to make it with embedded contexts.Secondly,the rating vectors with embedded contexts were used as service feature vectors to construct training set,meanwhile the dimension of service feature vector were not increased by the introduction of contexts.Thirdly,a separation hyperplane for active user was acquired based on training set using SVM,and then the SVM prediction model was built.Finally,the distances between the feature vector points representing the active users' unused services and the hyperplane were calculated.Considering the distances and the recommendation of similar users,the service list was recommended.The experimental results further demonstrate that the proposed method has better recommendation accuracy under different rating matrix densities and can reduce recommendation time.…”
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Service recommendation method based on context-embedded support vector machine by Chenyang ZHAO, Junling WANG

“…Combined with contexts and SVM,a service recommendation method based on context-embedded support vector machine (SRM-CESVM) was proposed.Firstly,according to the different contexts,the user rating matrix was modified to make it with embedded contexts.Secondly,the rating vectors with embedded contexts were used as service feature vectors to construct training set,meanwhile the dimension of service feature vector were not increased by the introduction of contexts.Thirdly,a separation hyperplane for active user was acquired based on training set using SVM,and then the SVM prediction model was built.Finally,the distances between the feature vector points representing the active users' unused services and the hyperplane were calculated.Considering the distances and the recommendation of similar users,the service list was recommended.The experimental results further demonstrate that the proposed method has better recommendation accuracy under different rating matrix densities and can reduce recommendation time.…”
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The development of the generative adversarial supporting vector machine for molecular property generation by Qing Lu

“…However, it has a large hyper-parameter space, which makes it difficult for training. In this work, we propose a new generative model by introducing the supporting vector machine into the GAN architecture. …”
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Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms by Qonita Ilmi Awalin, Ika Hesti Agustin, Alfian Futuhul Hadi, Dafik Dafik, R. Sunder

“…To categorize patient diagnosis data related to Chronic Kidney Disease (CKD), this study compares the classification performance of Support Vector Machines (SVM) enhanced by Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). …”
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The Method for Reducing the Term Vector Size for Category Classification of Text Documents by Golub T.V., Tiahunova M. Yu.

“…The article proposes a method for reducing time necessary for subsuming a certain document in order to classify the text documents by reducing the term vector size of certain categories. According to the method, the term weight factors were calculated for each classification category to implement subsuming process at the stage of training a certain system. …”
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FedSVD: Asynchronous Federated Learning With Stale Weight Vector Decomposition by Giwon Sur, Hyejin Kim, Seunghyun Yoon, Hyuk Lim

“…To address this staleness problem, we propose FedSVD, a method that leverages vector decomposition of stale weights. FedSVD evaluates each client’s trained weight in terms of their staleness relative to the current global model and decomposes the weights into two vectors: one pointing in the direction of the previous global model update, and another orthogonal to it. …”
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Lithology Identification of Buried Hill Reservoirs Based on Support Vector Machine by GAO Yongde, WU Jinbo, SUN Dianqiang

“…To address this issue, we selecte the HZ depression as the study area and propose a lithology identification model based on the support vector machine (SVM) algorithm. SVM is well-suited for handling high-dimensional data and exhibits strong generalization capability, while being relatively simple to implement, making it ideal for identifying complex reservoir lithology. …”
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