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Relevant bibliographies by topics / Support Vector Machines Classification

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Journal articles

Academic literature on the topic 'Support Vector Machines Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Support Vector Machines Classification"

1

Leporini, Roberto, and Davide Pastorello. "Support Vector Machines with Quantum State Discrimination." Quantum Reports 3, no. 3 (2021): 482–99. http://dx.doi.org/10.3390/quantum3030032.

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We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vectors have to be encoded in quantum states represented by density operators. Classification algorithms inspired by quantum state discrimination and implemented on classic computers have been recently proposed. We focus on the implementation of a known quantum-inspired classifier based on Helstrom state discrimination showing its connection with support vector machines and how to make the classification more efficient in terms of space and time acting on quantum encoding. In some cases, traditional methods provide better results. Moreover, we discuss the quantum-inspired nearest mean classification.

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2

Rang. "Poetry Classification Using Support Vector Machines." Journal of Computer Science 8, no. 9 (2012): 1441–46. http://dx.doi.org/10.3844/jcssp.2012.1441.1446.

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3

Karlsen, Robert E. "Target classification via support vector machines." Optical Engineering 39, no. 3 (2000): 704. http://dx.doi.org/10.1117/1.602417.

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4

Kwang In Kim, Keechul Jung, Se Hyun Park, and Hang Joon Kim. "Support vector machines for texture classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 11 (2002): 1542–50. http://dx.doi.org/10.1109/tpami.2002.1046177.

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5

Watanachaturaporn, Pakorn, Manoj K. Arora, and Pramod K. Varshney. "Multisource Classification Using Support Vector Machines." Photogrammetric Engineering & Remote Sensing 74, no. 2 (2008): 239–46. http://dx.doi.org/10.14358/pers.74.2.239.

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6

Baly, R., and H. Hajj. "Wafer Classification Using Support Vector Machines." IEEE Transactions on Semiconductor Manufacturing 25, no. 3 (2012): 373–83. http://dx.doi.org/10.1109/tsm.2012.2196058.

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7

Xia, Tian. "Support Vector Machine Based Educational Resources Classification." International Journal of Information and Education Technology 6, no. 11 (2016): 880–83. http://dx.doi.org/10.7763/ijiet.2016.v6.809.

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8

Pernes, Diogo, Kelwin Fernandes, and Jaime Cardoso. "Directional Support Vector Machines." Applied Sciences 9, no. 4 (2019): 725. http://dx.doi.org/10.3390/app9040725.

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Several phenomena are represented by directional—angular or periodic—data; from time references on the calendar to geographical coordinates. These values are usually represented as real values restricted to a given range (e.g., [ 0 , 2 π ) ), hiding the real nature of this information. In order to handle these variables properly in supervised classification tasks, alternatives to the naive Bayes classifier and logistic regression were proposed in the past. In this work, we propose directional-aware support vector machines. We address several realizations of the proposed models, studying their kernelized counterparts and their expressiveness. Finally, we validate the performance of the proposed Support Vector Machines (SVMs) against the directional naive Bayes and directional logistic regression with real data, obtaining competitive results.

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9

Abe, Shigeo. "Minimal Complexity Support Vector Machines for Pattern Classification." Computers 9, no. 4 (2020): 88. http://dx.doi.org/10.3390/computers9040088.

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Minimal complexity machines (MCMs) minimize the VC (Vapnik-Chervonenkis) dimension to obtain high generalization abilities. However, because the regularization term is not included in the objective function, the solution is not unique. In this paper, to solve this problem, we discuss fusing the MCM and the standard support vector machine (L1 SVM). This is realized by minimizing the maximum margin in the L1 SVM. We call the machine Minimum complexity L1 SVM (ML1 SVM). The associated dual problem has twice the number of dual variables and the ML1 SVM is trained by alternatingly optimizing the dual variables associated with the regularization term and with the VC dimension. We compare the ML1 SVM with other types of SVMs including the L1 SVM using several benchmark datasets and show that the ML1 SVM performs better than or comparable to the L1 SVM.

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10

BAE, Ji-Sang, and Jong-Ok KIM. "Multiclass Probabilistic Classification for Support Vector Machines." IEICE Transactions on Information and Systems E98.D, no. 6 (2015): 1251–55. http://dx.doi.org/10.1587/transinf.2014edl8167.

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