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Статті в журналах з теми "Structured Support Vector Machine"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Kim, Kyoungok, and Daewon Lee. "Inductive manifold learning using structured support vector machine." Pattern Recognition 47, no. 1 (January 2014): 470–79. http://dx.doi.org/10.1016/j.patcog.2013.07.011.

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2

Zhang, Shunli, Yao Sui, Sicong Zhao, and Li Zhang. "Graph-Regularized Structured Support Vector Machine for Object Tracking." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 6 (June 2017): 1249–62. http://dx.doi.org/10.1109/tcsvt.2015.2513659.

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3

Sharma, Manoj Kumar, and Vijaypal Singh Dhaka. "Segmentation of handwritten words using structured support vector machine." Pattern Analysis and Applications 23, no. 3 (September 16, 2019): 1355–67. http://dx.doi.org/10.1007/s10044-019-00843-x.

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4

Cuong, Nguyen The, and Huynh The Phung. "WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE." Journal of Computer Science and Cybernetics 37, no. 1 (March 29, 2021): 43–56. http://dx.doi.org/10.15625/1813-9663/37/1/15396.

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Анотація:

In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation on the capability of simulation of data trends. Structural Twin Support Vector Machine (S-TWSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Therefore, the capability of S-TWSVM’s data simulation is better than that of TWSVM. However, for the datasets where each class consists of clusters of different trends, the S-TWSVM’s data simulation capability seems restricted. Besides, the training time of S-TWSVM has not been improved compared to TWSVM. This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.

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5

Jeon, Yoondeok, Jiwoo Oh, Seungjae Lim, Yewon Choi, Sungmoon Kim, and Taeseon Yoon. "Analysis of Structural Relationship between Immunodeficiency Viruses Using Support Vector Machine." International Journal of Computer Theory and Engineering 7, no. 1 (February 2014): 46–50. http://dx.doi.org/10.7763/ijcte.2015.v7.928.

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6

Qu, Qiang, Ming Qi Chang, Lei Xu, Yue Wang, and Shao Hua Lu. "Support Vector Machine-Based Aqueduct Safety Assessment." Advanced Materials Research 368-373 (October 2011): 531–36. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.531.

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Анотація:

According to water power, structure and foundation conditions of aqueduct, it has established aqueduct safety assessment indicator system and standards. Based on statistical learning theory, support vector machine shifts the learning problems into a convex quadratic programming problem with structural risk minimization criterion, which could get the global optimal solution, and be applicable to solving the small sample, nonlinearity classification and regression problems. In order to evaluate the safety condition of aqueduct, it has established the aqueduct safety assessment model which is based on support vector machine. It has divided safety standards into normal, basically normal, abnormal and dangerous. According to the aqueduct safety assessment standards and respective evaluation level, the sample set is generated randomly, which is used to build a pair of classifier with many support vectors. The results show that the method is feasible, and it has a good application prospect in irrigation district canal building safety assessment.

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7

ZHANG, LI, WEI-DA ZHOU, TIAN-TIAN SU, and LI-CHENG JIAO. "DECISION TREE SUPPORT VECTOR MACHINE." International Journal on Artificial Intelligence Tools 16, no. 01 (February 2007): 1–15. http://dx.doi.org/10.1142/s0218213007003163.

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A new multi-class classifier, decision tree SVM (DTSVM) which is a binary decision tree with a very simple structure is presented in this paper. In DTSVM, a problem of multi-class classification is decomposed into a series of ones of binary classification. Here, the binary decision tree is generated by using kernel clustering algorithm, and each non-leaf node represents one binary classification problem. By compared with the other multi-class classification methods based on the binary classification SVMs, the scale and the complexity of DTSVM are less, smaller number of support vectors are needed, and has faster test speed. The final simulation results confirm the feasibility and the validity of DTSVM.

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8

Yamamoto, Maeri, Epifanio Bagarinao, Itaru Kushima, Tsutomu Takahashi, Daiki Sasabayashi, Toshiya Inada, Michio Suzuki, Tetsuya Iidaka, and Norio Ozaki. "Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites." PLOS ONE 15, no. 11 (November 24, 2020): e0239615. http://dx.doi.org/10.1371/journal.pone.0239615.

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Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.

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9

Hou, Qiuling, Ling Zhen, Naiyang Deng, and Ling Jing. "Novel Grouping Method-based support vector machine plus for structured data." Neurocomputing 211 (October 2016): 191–201. http://dx.doi.org/10.1016/j.neucom.2016.03.086.

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10

Hao, Pei-Yi, Jung-Hsien Chiang, and Yen-Hsiu Lin. "A new maximal-margin spherical-structured multi-class support vector machine." Applied Intelligence 30, no. 2 (October 18, 2007): 98–111. http://dx.doi.org/10.1007/s10489-007-0101-z.

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11

Shi, Xu Chao, Qi Xia Liu, and Xiu Juan Lv. "Application of SVM in Predicting the Strength of Cement Stabilized Soil." Applied Mechanics and Materials 160 (March 2012): 313–17. http://dx.doi.org/10.4028/www.scientific.net/amm.160.313.

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Support Vector Machine is a powerful machine learning technique based on statistical learning theory. This paper investigates the potential of support vector machines based regression approach to model the strength of cement stabilized soil from test dates. Support Vector Machine model is proposed to predict compressive strength of cement stabilized soil. And the effects of selecting kernel function on Support Vector Machine modeling are also analyzed. The results show that the Support Vector Machine is more precise in measuring the strength of cement than traditional methods. The Support Vector Machine method has advantages in its simple structure，excellent capability in studying and good application prospects, also it provide us with a novel method of measuring the strength of cement stabilized soil.

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12

Mandle, Anil Kumar. "Protein Structure Prediction Using Support Vector Machine." International Journal on Soft Computing 3, no. 1 (February 29, 2012): 67–78. http://dx.doi.org/10.5121/ijsc.2012.3106.

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13

Joachims, Thorsten, Thomas Hofmann, Yisong Yue, and Chun-Nam Yu. "Predicting structured objects with support vector machines." Communications of the ACM 52, no. 11 (November 2009): 97–104. http://dx.doi.org/10.1145/1592761.1592783.

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14

Wu, Seongho, Hui Zou, and Ming Yuan. "Structured variable selection in support vector machines." Electronic Journal of Statistics 2 (2008): 103–17. http://dx.doi.org/10.1214/07-ejs125.

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15

Li, Kai, and Jie Li. "Structural Fuzzy Multi-class Support Vector Machine." Journal of Physics: Conference Series 1631 (September 2020): 012188. http://dx.doi.org/10.1088/1742-6596/1631/1/012188.

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16

Qi, Zhiquan, Yingjie Tian, and Yong Shi. "Structural twin support vector machine for classification." Knowledge-Based Systems 43 (May 2013): 74–81. http://dx.doi.org/10.1016/j.knosys.2013.01.008.

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17

Li, Hong-shuang, Zhen-zhou Lü, and Zhu-feng Yue. "Support vector machine for structural reliability analysis." Applied Mathematics and Mechanics 27, no. 10 (October 2006): 1295–303. http://dx.doi.org/10.1007/s10483-006-1001-z.

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18

T, Sai Tejeshwar Reddy. "An Enhanced Novel GA-based Malware Detection in End Systems Using Structured and Unstructured Data by Comparing Support Vector Machine and Neural Network." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 1514–25. http://dx.doi.org/10.47059/revistageintec.v11i2.1777.

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Анотація:

Aim: The aim of the work is to perform android malware detection using Structured and Unstructured data by comparing Neural Network algorithms and SVM. Materials and Methods: consider two groups such as Support Vector Machine and Neural Network. For each algorithm take N=10 samples from the dataset collected and perform two iterations on each algorithm to identify the Malware Detection. Result: The accuracy results of the Neural Network model has potential up to (82.91%) and the Support Vector Machine algorithm has an accuracy of (79.67%) for Android malware detection with the significance value of (p=0.007). Conclusion: classification of android malware detection using Neural Network algorithm shows better accuracy than SVM.

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19

Li, Xiao, Xin Liu, Clyde Zhengdao Li, Zhumin Hu, Geoffrey Qiping Shen, and Zhenyu Huang. "Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement." Structural Health Monitoring 18, no. 3 (April 23, 2018): 715–24. http://dx.doi.org/10.1177/1475921718767935.

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Анотація:

Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.

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20

Ju, Hongmei, Yafang Zhang та Ye Zhao. "υ-Nonparallel parametric margin fuzzy support vector machine". Journal of Intelligent & Fuzzy Systems 40, № 6 (21 червня 2021): 11731–47. http://dx.doi.org/10.3233/jifs-202869.

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Анотація:

Classification problem is an important research direction in machine learning. υ-nonparallel support vector machine (υ-NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. However, when solving classification problems, υ-NPSVM will encounter the problem of sample noises and heteroscedastic noise structure, which will affect its performance. In this paper, two improvements are made on the υ-NPSVM model, and a υ-nonparallel parametric margin fuzzy support vector machine (par-υ-FNPSVM) is established. On the one hand, for the noises that may exist in the data set, the neighbor information is used to add fuzzy membership to the samples, so that the contribution of each sample to the classification is treated differently. On the other hand, in order to reduce the effect of heteroscedastic structure, an insensitive loss function is introduced. The advantages of the new model are verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of it.

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21

Hanna, Sean. "Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 4 (September 19, 2007): 351–66. http://dx.doi.org/10.1017/s0890060407000327.

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AbstractStructural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods.

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22

Li, Weisheng, Yanquan Chen, Bin Xiao, and Chen Feng. "Dual linear structured support vector machine tracking method via scale correlation filter." Journal of Electronic Imaging 27, no. 01 (February 26, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.1.013027.

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23

Chen, Jian Guo, and Zhao Guang Li. "Application of Support Vector Machine in Springback Forecasting for Steel Structure." Applied Mechanics and Materials 166-169 (May 2012): 1366–69. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1366.

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Анотація:

Support vector machine is applied to springback forecasting for steel structure in the paper. In the steel structure, pressure-pad-force, friction coefficient and die filleted corner have a certain influence on springback amount.We employ BP neural network to compare with support vector machine to show the superiority of support vector machine in this study. Finally,we give the comparison of the prediction error of springback for steel structure between support vector machine and BP neural network. Evidently,the springback prediction for steel structure of support vector machine is better than that of BP neural network.

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24

ZHAO, YING, and GEORGE KARYPIS. "PREDICTION OF CONTACT MAPS USING SUPPORT VECTOR MACHINES." International Journal on Artificial Intelligence Tools 14, no. 05 (October 2005): 849–65. http://dx.doi.org/10.1142/s0218213005002429.

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Contact map prediction is of great interest for its application in fold recognition and protein 3D structure determination. In this paper we present a contact-map prediction algorithm that employs Support Vector Machines as the machine learning tool and incorporates various features such as sequence profiles and their conservations, correlated mutation analysis based on various amino acid physicochemical properties, and secondary structure. In addition, we evaluated the effectiveness of the different features on contact map prediction for different fold classes. On average, our predictor achieved a prediction accuracy of 0.224 with an improvement over a random predictor of a factor 11.7, which is better than reported studies. Our study showed that predicted secondary structure features play an important roles for the proteins containing beta-structures. Models based on secondary structure features and correlated mutation analysis features produce different sets of predictions. Our study also suggests that models learned separately for different protein fold families may achieve better performance than a unified model.

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25

Kang, Lan Lan, and Wen Liang Cao. "Support Vector Machine and its Application Status." Advanced Materials Research 1030-1032 (September 2014): 1814–17. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1814.

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Анотація:

Support vector machine is a beginning of the 1990s, based on statistical learning theory proposed new machine learning method, which structural risk minimization principle as the theoretical basis, by appropriately selecting a subset of functions and discriminant function in the subset, so the actual risk of learning machine to a minimum, to ensure that the limited training samples obtained through a small error classifier, an independent test set for testing error remains small. In this paper, support vector machine theory, algorithm, application status, etc. are discussed in detail.

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26

Shi, Ruijia, and Xiuzhen Hu. "Predicting Enzyme Subclasses by Using Support Vector Machine with Composite Vectors." Protein & Peptide Letters 17, no. 5 (May 1, 2010): 599–604. http://dx.doi.org/10.2174/092986610791112710.

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27

Hernault, Hugo, Helmut Prendinger, David A. du Verle, and Mitsuru Ishizuka. "HILDA: A Discourse Parser Using Support Vector Machine Classification." Dialogue & Discourse 1, no. 3 (December 10, 2010): 1–33. http://dx.doi.org/10.5087/dad.2010.003.

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Discourse structures have a central role in several computational tasks, such as question-answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classification. SVM classifiers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear time complexity. Importantly, our parser can parse entire texts, whereas the publicly available parser SPADE (Soricut and Marcu 2003) is limited to sentence level analysis. HILDA outperforms other discourse parsers for tree structure construction and discourse relation labeling. For the discourse parsing task, our system reaches 78.3% of the performance level of human annotators. Compared to a state-of-the-art rule-based discourse parser, our system achieves a performance increase of 11.6%.

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28

Chen, Dandan, Yingjie Tian, and Xiaohui Liu. "Structural nonparallel support vector machine for pattern recognition." Pattern Recognition 60 (December 2016): 296–305. http://dx.doi.org/10.1016/j.patcog.2016.04.017.

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29

Radhika, D. "Virtual Machine Task Classification Using Support Vector Machine and Improved MFO Based Task Scheduling." Journal of University of Shanghai for Science and Technology 24, no. 02 (February 9, 2022): 1–15. http://dx.doi.org/10.51201/jusst/22/0159.

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Анотація:

The processing of data in Big data computing necessitates a significant number of CPU cycles and network bandwidth. Dataflow is a huge data processing programming model that comprises of jobs structured in a graph structure. Scheduling these jobs is one of the most active study fields, with the primary goal of allocating tasks based on available resources. It is critical to efficiently schedule jobs in a way that minimizes task completion time and maximizes resource utilization. In recent years, many research works on task scheduling problems in cloud computing and various heuristic approaches have been evolved which the thesis focuses upon. Most of these efforts are focused on improving the performance such as minimizing the makespan and efficient utilization of cloud resources that benefits the cloud users and the providers. In this paper, Big Data analysis processing in cloud environment for efficient dynamic task scheduling by using different techniques as machine learning classifier and optimization approach. In machine learning classifier as Support Vector Machine (SVM) for classification of virtual machine task classification. In this classifier can classify the incoming request efficiently and reduce the makespan and execution time. Further, we used moth flame optimization technique for allocating the classified task from SVM classifier. In this proposed system have classification of virtual machine (VM) task and evaluate the resource allocation decision making procedures. In this experiment, we carried out by using cloud simulation environment to evaluate and analysis the proposed method. In this proposed scheme can efficiently reduce the makespan time and load balancing to improve the better VM Classification. And aslo we compare the proposed moth flame optimization (MFO) with min-max algorithm and particle swarm optimization to compare the performance respectively.

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30

Giustolisi, Orazio. "Using a multi-objective genetic algorithm for SVM construction." Journal of Hydroinformatics 8, no. 2 (March 1, 2006): 125–39. http://dx.doi.org/10.2166/hydro.2006.016b.

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Анотація:

Support Vector Machines are kernel machines useful for classification and regression problems. In this paper, they are used for non-linear regression of environmental data. From a structural point of view, Support Vector Machines are particular Artificial Neural Networks and their training paradigm has some positive implications. In fact, the original training approach is useful to overcome the curse of dimensionality and too strict assumptions on statistics of the errors in data. Support Vector Machines and Radial Basis Function Regularised Networks are presented within a common structural framework for non-linear regression in order to emphasise the training strategy for support vector machines and to better explain the multi-objective approach in support vector machines' construction. A support vector machine's performance depends on the kernel parameter, input selection and ε-tube optimal dimension. These will be used as decision variables for the evolutionary strategy based on a Genetic Algorithm, which exhibits the number of support vectors, for the capacity of machine, and the fitness to a validation subset, for the model accuracy in mapping the underlying physical phenomena, as objective functions. The strategy is tested on a case study dealing with groundwater modelling, based on time series (past measured rainfalls and levels) for level predictions at variable time horizons.

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31

Zhang, Ruiting, and Zhijian Zhou. "A Fuzzy Least Squares Support Tensor Machines in Machine Learning." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (December 14, 2015): 4. http://dx.doi.org/10.3991/ijet.v10i8.5203.

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Анотація:

In the machine learning field, high-dimensional data are often encountered in the real applications. Most of the traditional learning algorithms are based on the vector space model, such as SVM. Tensor representation is useful to the over fitting problem in vector-based learning, and tensor-based algorithm requires a smaller set of decision variables as compared to vector-based approaches. We also would require that the meaningful training points must be classified correctly and would not care about some training points like noises whether or not they are classified correctly. To utilize the structural information present in high dimensional features of an object, a tensor-based learning framework, termed as Fuzzy Least Squares support tensor machine (FLSSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of FLSSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in ORL database and Yale database. The FLSSTM outperforms other tensor-based algorithms, for example, LSSTM, especially when training size is small.

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32

Abo Zidan, Rawan, and George Karraz. "Gaussian Pyramid for Nonlinear Support Vector Machine." Applied Computational Intelligence and Soft Computing 2022 (May 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/5255346.

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Анотація:

Support vector machine (SVM) is one of the most efficient machine learning tools, and it is fast, simple to use, reliable, and provides accurate classification results. Despite its generalization capability, SVM is usually posed as a quadratic programming (QP) problem to find a separation hyperplane in nonlinear cases. This needs huge quantities of computational time and memory for large datasets, even for moderately sized ones. SVM could be used for classification tasks whose number of samples is limited but does not scale well to large datasets. The idea is to solve this problem by a smoothing technique to get a new smaller dataset representing the original one. This paper proposes a fast and less time and memory-consuming algorithm to solve the problems represented by a nonlinear support vector machine tool, based on generating a Gaussian pyramid to minimize the size of the dataset. The reduce operation between dataset points and the Gaussian pyramid is reformulated to get a smoothed copy of the original dataset. The new dataset points after passing the Gaussian pyramid will be closed to each other, and this will minimize the degree of nonlinearity in the dataset, and it will be 1/4 of the size of the original large dataset. The experiments demonstrate that our proposed techniques can reduce the classical SVM tool complexity, more accurately, and are applicable in real time.

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33

Sepahvand, Kian K. "Structural Damage Detection Using Supervised Nonlinear Support Vector Machine." Journal of Composites Science 5, no. 11 (November 18, 2021): 303. http://dx.doi.org/10.3390/jcs5110303.

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Анотація:

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.

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34

JIANG, Hua, and Yu-shun QI. "Active learning for multi-label classification based on sphere structured support vector machine." Journal of Computer Applications 32, no. 5 (April 23, 2013): 1359–61. http://dx.doi.org/10.3724/sp.j.1087.2012.01359.

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35

Hardoon, David R., and John Shawe-Taylor. "Decomposing the tensor kernel support vector machine for neuroscience data with structured labels." Machine Learning 79, no. 1-2 (December 9, 2009): 29–46. http://dx.doi.org/10.1007/s10994-009-5159-x.

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36

Shi, Haifa, Xinbin Zhao, Ling Zhen, and Ling Jing. "Twin Bounded Support Tensor Machine for Classification." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 01 (December 30, 2015): 1650002. http://dx.doi.org/10.1142/s0218001416500026.

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Анотація:

The traditional vector-based classifiers, such as support vector machine (SVM) and twin support vector machine (TSVM), cannot handle tensor data directly and may not utilize the data informations effectively. In this paper, we propose a novel classifier based on tensor data, called twin bounded support tensor machine (TBSTM) which is an extension of twin bounded support vector machine (TBSVM). Similar to TBSVM, TBSTM gets two hyperplanes and obtains the solution by solving two quadratic programming problems (QPPs). The computational complexity of each QPPs is smaller than that of support tensor machine (STM). TBSTM not only retains the advantage of TBSVM, but also has its unique superior characteristics: (1) it makes full use of the structure information of data; (2) it has acceptable or better classification accuracy compared to STM, TBSVM and SVM; (3) the computational cost is basically less than STM; (4) it can deal with large data that TBSVM is not easy to achieve, especially for small-sample-size (S3) problems; (5) it adopts alternating successive over relaxation iteration (ASOR) method to solve optimization problems which accelerates the pace of training. Finally, we demonstrate the effectiveness and superiority by the experiments based on vector and tensor data.

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Ma, Guang Yue. "Steel Pip Corrosion Forecasting Based on Support Vector Machine." Applied Mechanics and Materials 166-169 (May 2012): 1002–6. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1002.

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Анотація:

BP neural network has some shorcomings,such as local extreme. Support vector machine is a novel statistical learning algorithm,which is based on the principle of structural risk minimization. In the paper, support vector machine is used to perform steel pip corrosion forecasting.The collected steel pip corrosion forecasting experimental data are given,among which corrosion deeps from 8ths to 11ths are used to test the proposed prediction model. BP neural network is applied to steel pip corrosion deep forecasting,which is used to compare with support vector machine to show the superiority of support vector machine in steel pip corrosion forecasting.The comparison of the prediction error of steel pip corrosion deep between support vector machine and BP neural network is given. It can be seen that the prediction ability for steel pip corrosion deep of support vector machine is better than that of BP neural network

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Yang, Hai Ying, and Yi Feng Dong. "Modelling Concrete Strength Using Support Vector Machines." Applied Mechanics and Materials 438-439 (October 2013): 170–73. http://dx.doi.org/10.4028/www.scientific.net/amm.438-439.170.

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Support vector machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. A SVM model is presented to predict compressive strength of concrete at 28 days in this paper. A total of 20 data sets were used to train, whereas the remaining 10 data sets were used to test the created model. Radial basis function based on support vector machines was used to model the compressive strength and results were compared with a generalized regression neural network approach. The results of this study showed that the SVM approach has the potential to be a practical tool for predicting compressive strength of concrete at 28 days.

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Xiaoyan, Zhang, and Wang Qiuqiu. "An Improved Hybrid Structure Multi-classification Support Vector Machine." Journal of Physics: Conference Series 1187, no. 3 (April 2019): 032096. http://dx.doi.org/10.1088/1742-6596/1187/3/032096.

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40

IWASAKI, Atsushi, Takahiro SANO, Akira TODOROKI, and Shinsuke SAKAI. "Delamination diagnosis of CFRP Structure using Support Vector Machine." Proceedings of the 1992 Annual Meeting of JSME/MMD 2004 (2004): 287–88. http://dx.doi.org/10.1299/jsmezairiki.2004.0_287.

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41

Zhang, S. W., Q. Pan, H. C. Zhang, Y. L. Zhang, and H. Y. Wang. "Classification of protein quaternary structure with support vector machine." Bioinformatics 19, no. 18 (December 10, 2003): 2390–96. http://dx.doi.org/10.1093/bioinformatics/btg331.

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42

Waegeman, Willem, Bernard De Baets, and Luc Boullart. "Learning layered ranking functions with structured support vector machines." Neural Networks 21, no. 10 (December 2008): 1511–23. http://dx.doi.org/10.1016/j.neunet.2008.07.008.

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43

Yang, Biao, Wei Li, Li Jun Liu, Jin Hui Peng, Li Bo Zhang, Shi Min Zhang, and Sheng Hui Guo. "Support Vector Machine and its Predicting Stability of Partially Stabilized Zirconia by Microwave Heating Preparation." Advanced Materials Research 382 (November 2011): 281–88. http://dx.doi.org/10.4028/www.scientific.net/amr.382.281.

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Анотація:

Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVR machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave preparing partially stabilized zirconia (PSZ) and built the stability prediction model, the better prediction accuracy and the better fitting results are verified and analyzed. This is conducted to elucidate the good generalization performance of SVMs, specially good for dealing with nonlinear data.

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44

A, Dr Arivoli, and Sonali Pandey. "SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE BASED ON FEATURE SELECTION AND SEMANTIC ANALYSIS." International Research Journal of Computer Science 8, no. 8 (August 30, 2021): 209–14. http://dx.doi.org/10.26562/irjcs.2021.v0808.009.

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Social media is a popular network through which user can share their reviews about various topics, news, products etc. People use internet to access or update reviews so it is necessary to express opinion. Sentiment analysis is to classify these reviews based on its opinion as either positive or negative category. First we have preprocessed the dataset to convert unstructured reviews into structured form. Then we have used lexicon based approach to convert structured review into numerical score value. In lexicon based approach we have preprocessed dataset using feature selection and semantic analysis. Stop word removal, stemming, POS tagging and calculating sentiment score with help of SentiWordNet dictionary have been done in preprocessing part. Then we have applied classification algorithm to classify opinion as either positive or negative. Support vector machine algorithm is used to classify reviews where RBF kernel SVM is modified by its hyper parameters which are soft margin constant C , Gamma γ. So optimized SVM gives good result than SVM and naïve bayes. At last we have compared performance of all classifier with respect to accuracy.

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45

Gan, Xu Sheng, Jing Shun Duanmu, and Jian Guo Gao. "Longitudinal Aerodynamic Modeling Based on Support Vector Machine." Applied Mechanics and Materials 327 (June 2013): 290–93. http://dx.doi.org/10.4028/www.scientific.net/amm.327.290.

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Анотація:

In order to accurately depict the longitudinal aerodynamic characteristics of flight vehicle, a new aerodynamic modeling method based on Support Vector Machine (SVM) is proposed. SVM is a machine learning method that solves the problem by mean of optimization method on the basis of statistics learning theory, and introduces the structural risk minimization to coordinate the relationship between fitting degree and generalization ability, and the training samples are mapped into high dimensional feature space for linear regression, which can solve the practical problems such as nonlinearity, high dimension, over-learning, local minima, curse of dimensionality and so on. The experimental result shows that the method has a good modeling accuracy and speed training, and also is easy to implement. It is fully competent for longitudinal aerodynamic modeling for flight vehicle.

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46

Sheykh Mohammadi, Fatemeh, and Ali Amiri. "TS-WRSVM: twin structural weighted relaxed support vector machine." Connection Science 31, no. 3 (February 3, 2019): 215–43. http://dx.doi.org/10.1080/09540091.2019.1573418.

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47

Pan, Xianli, Yao Luo, and Yitian Xu. "K-nearest neighbor based structural twin support vector machine." Knowledge-Based Systems 88 (November 2015): 34–44. http://dx.doi.org/10.1016/j.knosys.2015.08.009.

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48

Chen, Gang, Ran Xu, and Zhi Yang. "Deep ranking structural support vector machine for image tagging." Pattern Recognition Letters 105 (April 2018): 30–38. http://dx.doi.org/10.1016/j.patrec.2017.09.012.

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49

Zhu, Changxing, and Hongbo Zhao. "Least square support vector machine for structural reliability analysis." International Journal of Computer Applications in Technology 53, no. 1 (2016): 51. http://dx.doi.org/10.1504/ijcat.2016.073610.

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50

Xu, Yitian, Xianli Pan, Zhijian Zhou, Zhiji Yang, and Yuqun Zhang. "Structural least square twin support vector machine for classification." Applied Intelligence 42, no. 3 (November 14, 2014): 527–36. http://dx.doi.org/10.1007/s10489-014-0611-4.

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