Relevant bibliographies by topics / Fuzzy Support Vector Machine (FSVM)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fuzzy Support Vector Machine (FSVM)'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Fuzzy Support Vector Machine (FSVM).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Fuzzy Support Vector Machine (FSVM)"

1

Zhang, Rui, Tong Bo Liu, and Ming Wen Zheng. "An New Fuzzy Support Vector Machine for Binary Classification." Advanced Materials Research 433-440 (January 2012): 2856–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2856.

Full text
Abstract:
In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classification in the three methods stated above.
APA, Harvard, Vancouver, ISO, and other styles
2

Zhou, Huaping, and Huangli Qin. "Self-Adjusting Fuzzy Support Vector Machine Based on Analysis of Potential Support Vector Sample Point." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (2019): 1959035. http://dx.doi.org/10.1142/s0218001419590353.

Full text
Abstract:
Fuzzy support vector machine (FSVM) is a part of machine learning with its good classification effect. So far, there are two most commonly used FSVM models: FSVM on account of class core and fuzzy support vector machine on account of hyperplane that is over class core. Each has its own problems: FSVM on account of class core are dependent on the geometric shape of sample sets. Although FSVM on account of hyperplane that is over class core can solve the above problems to some extent. However, this algorithm has low generalization ability and high time complexity. Therefore, Inspired by these tw
APA, Harvard, Vancouver, ISO, and other styles
3

Shanmugapriya, P., and Y. Venkataramani. "Analysis of Speaker Verification System Using Support Vector Machine." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 10 (2017): 6531–42. http://dx.doi.org/10.24297/jac.v13i10.5839.

Full text
Abstract:
The integration of GMM- super vector and Support Vector Machine (SVM) has become one of most popular strategy in text-independent speaker verification system. This paper describes the application of Fuzzy Support Vector Machine (FSVM) for classification of speakers using GMM-super vectors. Super vectors are formed by stacking the mean vectors of adapted GMMs from UBM using maximum a posteriori (MAP). GMM super vectors characterize speaker’s acoustic characteristics which are used for developing a speaker dependent fuzzy SVM model. Introducing fuzzy theory in support vector machine yields be
APA, Harvard, Vancouver, ISO, and other styles
4

Ke, Hong Xia, Guo Dong Liu, and Guo Bing Pan. "Fuzzy Support Vector Machine for PolSAR Image Classification." Advanced Materials Research 639-640 (January 2013): 1162–67. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.1162.

Full text
Abstract:
Fully Polarimetric Synthetic Aperture Radar (PolSAR) image classification, with the complexity for its data’s scattering mechanism and statistical property, has expected to be performed by an automatic categorization. This paper presents a supervised method called Fuzzy support vector machine (FSVM), which is a variant of the SVM algorithm to classify the PolSAR image data. In order to take advantages of PolSAR data, five scattering features (entropy, total power, three Eigenvalues of Coherent Matrix: λ1,λ2,λ3) are input as original data space of the FSVM algorithm. The feasibility of this app
APA, Harvard, Vancouver, ISO, and other styles
5

Duan, Hua, and Yan Mei Hou. "A Solving Algorithm of Fuzzy Support Vector Machines Based on Determination of Membership." Advanced Materials Research 756-759 (September 2013): 3399–403. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3399.

Full text
Abstract:
In order to overcome the issues that Support Vector Machine is sensitive to the outlier and noise points, Fuzzy Support Vector Machine (FSVM) is proposed. The key issue to solve the FSVM is determinate the fuzzy membership. This paper gives an overview of construction algorithm of the fuzzy membership. We also give an algorithm to solve FSVM that is derived from improved-SMO algorithm.
APA, Harvard, Vancouver, ISO, and other styles
6

Yu, Lean. "Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/564213.

Full text
Abstract:
A least squares fuzzy support vector machine (LS-FSVM) model that integrates advantages of fuzzy support vector machine (FSVM) and least squares method is proposed for credit risk evaluation. In the proposed LS-FSVM model, the purpose of incorporating the concepts of fuzzy sets is to add generalization capability and outlier insensitivity, while the least squares method is adopted to reduce the computational complexity. For illustrative purposes, a real-world credit risk dataset is used to test the effectiveness and robustness of the proposed LS-FSVM methodology.
APA, Harvard, Vancouver, ISO, and other styles
7

Hou, Yuan Bin, Ning Li, Fan Guo, and Jing Chen. "Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector." Applied Mechanics and Materials 135-136 (October 2011): 547–52. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.547.

Full text
Abstract:
Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, tw
APA, Harvard, Vancouver, ISO, and other styles
8

Li, Kai, and Xiao Xia Lu. "A Rough Margin Based Fuzzy Support Vector Machine." Advanced Materials Research 204-210 (February 2011): 879–82. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.879.

Full text
Abstract:
By combining fuzzy support vector machine with rough set, we propose a rough margin based fuzzy support vector machine (RFSVM). It inherits the characteristic of the FSVM method and considers position of training samples of the rough margin in order to reduce overfitting due to noises or outliers. The new proposed algorithm finds the optimal separating hyperplane that maximizes the rough margin containing lower margin and upper margin. Meanwhile, the points lied on the lower margin have larger penalty than these in the boundary of the rough margin. Experiments on several benchmark datasets sho
APA, Harvard, Vancouver, ISO, and other styles
9

Gu, Xiaoqing, Tongguang Ni, and Hongyuan Wang. "New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/536434.

Full text
Abstract:
In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outli
APA, Harvard, Vancouver, ISO, and other styles
10

Chen, Hao Guang, Xiao Xi Li, and Da Xi Li. "New Fuzzy Support Vector Machine Method Based on Entropy and Ant-Colony Optimization." Applied Mechanics and Materials 380-384 (August 2013): 1580–84. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1580.

Full text
Abstract:
Concerning the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current Fuzzy Support Vector Machines (FSVM), a new FSVM based on entropy and Genetic Algorithm (GA) named EGFSVM was proposed in this paper. Making use of evaluation of entropy and intelligence of GA, EGFSVM enhances the classification capability and makes clustering center more suitable and membership more accurate. Experimental results show EGFSVM has better precision and classification performance, especially to multi-class and large scale data.
APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Fuzzy Support Vector Machine (FSVM)"

1

Kannan, Anand. "Performance evaluation of security mechanisms in Cloud Networks." Thesis, KTH, Kommunikationssystem, CoS, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99464.

Full text
Abstract:
Infrastructure as a Service (IaaS) is a cloud service provisioning model which largely focuses on data centre provisioning of computing and storage facilities. The networking aspects of IaaS beyond the data centre are a limiting factor preventing communication services that are sensitive to network characteristics from adopting this approach. Cloud networking is a new technology which integrates network provisioning with the existing cloud service provisioning models thereby completing the cloud computing picture by addressing the networking aspects. In cloud networking, shared network resourc
APA, Harvard, Vancouver, ISO, and other styles
2

Benbrahim, Houda. "A fuzzy semi-supervised support vector machine approach to hypertext categorization." Thesis, University of Portsmouth, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494145.

Full text
Abstract:
As the web expands exponentially, the need to put some order to its content becomes apparent. Hypertext categorization, that is the automatic classification of web documents into predefined classes, came to elevate humans from that task. The extra information available in a hypertext document poses new challenges for automatic categorization. HTML tags and linked neighbourhood all provide rich information for hypertext categorization that is no available in traditional text classification.
APA, Harvard, Vancouver, ISO, and other styles
3

Uslan, Volkan. "Support vector machine-based fuzzy systems for quantitative prediction of peptide binding affinity." Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/11170.

Full text
Abstract:
Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A
APA, Harvard, Vancouver, ISO, and other styles
4

OLIVEIRA, A. B. "Modelo de Predição para análise comparativa de Técnicas Neuro-Fuzzy e de Regressão." Universidade Federal do Espírito Santo, 2010. http://repositorio.ufes.br/handle/10/4218.

Full text
Abstract:
Made available in DSpace on 2016-08-29T15:33:12Z (GMT). No. of bitstreams: 1 tese_3521_.pdf: 2782962 bytes, checksum: d4b2294e5ee9ab86b7a35aec083af692 (MD5) Previous issue date: 2010-02-12<br>Os Modelos de Predição implementados pelos algoritmos de Aprendizagem de Máquina advindos como linha de pesquisa da Inteligência Computacional são resultantes de pesquisas e investigações empíricas em dados do mundo real. Neste contexto; estes modelos são extraídos para comparação de duas grandes técnicas de aprendizagem de máquina Redes Neuro-Fuzzy e de Regressão aplicadas no intuito de estimar um pa
APA, Harvard, Vancouver, ISO, and other styles
5

Abo, Al Ahad George, and Abbas Salami. "Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets." Thesis, Linköpings universitet, Produktionsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.

Full text
Abstract:
Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. Since it is hard for a human to make sense out of large amounts of data, different modeling techniques have been applied to extract useful information from financial databases, where machine learning techniques are among the most recent modeling techniques. Binary classifiers such as Support Vector Machines (SVMs) h
APA, Harvard, Vancouver, ISO, and other styles
6

Chida, Anjum A. "Protein Tertiary Model Assessment Using Granular Machine Learning Techniques." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/65.

Full text
Abstract:
The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same
APA, Harvard, Vancouver, ISO, and other styles
7

Thomas, Rodney H. "Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1053.

Full text
Abstract:
Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to
APA, Harvard, Vancouver, ISO, and other styles
8

Díaz, Jorge Luis Guevara. "Modelos de aprendizado supervisionado usando métodos kernel, conjuntos fuzzy e medidas de probabilidade." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03122015-155546/.

Full text
Abstract:
Esta tese propõe uma metodologia baseada em métodos de kernel, teoria fuzzy e probabilidade para tratar conjuntos de dados cujas observações são conjuntos de pontos. As medidas de probabilidade e os conjuntos fuzzy são usados para modelar essas observações. Posteriormente, graças a kernels definidos sobre medidas de probabilidade, ou em conjuntos fuzzy, é feito o mapeamento implícito dessas medidas de probabilidade, ou desses conjuntos fuzzy, para espaços de Hilbert com kernel reproduzível, onde a análise pode ser feita com algum método kernel. Usando essa metodologia, é possível fazer fren
APA, Harvard, Vancouver, ISO, and other styles
9

Hu, Linlin. "A novel hybrid technique for short-term electricity price forecasting in deregulated electricity markets." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4498.

Full text
Abstract:
Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting mo
APA, Harvard, Vancouver, ISO, and other styles
10

Chen, Xiujuan. "Computational Intelligence Based Classifier Fusion Models for Biomedical Classification Applications." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/26.

Full text
Abstract:
The generalization abilities of machine learning algorithms often depend on the algorithms’ initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fusion models using computational intelligence knowledge to combine different classifiers. The first fusion model called T1FFSVM combines multiple SVM classifiers through constructing a fuzzy logic system.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Fuzzy Support Vector Machine (FSVM)"

1

The nonlinear workbook: Chaos, fractals, cellular automata, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, fuzzy logic with C++, Java and symbolic C++ programs. 6th ed. World Scientific, 2015.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. World Scientific, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 3rd ed. World Scientific, 2005.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. World Scientific, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 4th ed. World Scientific, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques. Imperial College Press, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Fuzzy Support Vector Machine (FSVM)"

1

Zhang, Hao, Kang Li, and Cheng Wu. "Fuzzy Chance Constrained Support Vector Machine." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15621-2_30.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Benbrahim, Houda. "Fuzzy Semi-supervised Support Vector Machines." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ganji, Hamed, and Shahram Khadivi. "ASVMFC: Adaptive Support Vector Machine Based Fuzzy Classifier." In Information Retrieval Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25631-8_31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hao, Pei-Yi. "A New Fuzzy Support Vector Data Description Machine." In Modern Advances in Applied Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07455-9_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wu, Xiao, Yan Wei, and Xia Wu. "Improved Double Memberships of Fuzzy Support Vector Machine." In Lecture Notes in Electrical Engineering. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_99.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Yang, Tao, and Bo Hu. "Study of Multiuser Detection: The Support Vector Machine Approach." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_54.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hwang, Changha, Dug Hun Hong, Eunyoung Na, Hyejung Park, and Jooyong Shim. "Interval Regression Analysis Using Support Vector Machine and Quantile Regression." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Choi, YoungSik, and KiJoo Kim. "Video Summarization Using Fuzzy One-Class Support Vector Machine." In Computational Science and Its Applications – ICCSA 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24707-4_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wu, Kui, and Kim-Hui Yap. "Soft-Labeling Image Scheme Using Fuzzy Support Vector Machine." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76827-2_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hao, Yanyou, Zhongxian Chi, Deqin Yan, and Xun Yue. "An Improved Fuzzy Support Vector Machine for Credit Rating." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74784-0_50.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Fuzzy Support Vector Machine (FSVM)"

1

Lakshmanan, B., A. Jeril Priscilla, S. Ponni, and V. Sankari. "Evaluation of imbalanced datasets using fuzzy support vector machine-class imbalance learning (FSVM-CIL)." In 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2011. http://dx.doi.org/10.1109/icrtit.2011.5972431.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Xiao, Xiaoling, and Xiang Zhang. "An Improved Fuzzy Support Vector Machine." In 2009 International Symposium on Intelligent Ubiquitous Computing and Education, IUCE. IEEE, 2009. http://dx.doi.org/10.1109/iuce.2009.101.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Shilton, Alistair, and Daniel T. H. Lai. "Iterative Fuzzy Support Vector Machine Classification." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295570.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wu, Qing, and Kaiyue Sun. "An improved fuzzy twin support vector machine based on support vector." In 2017 13th IEEE Conference on Automation Science and Engineering (CASE 2017). IEEE, 2017. http://dx.doi.org/10.1109/coase.2017.8256256.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Xue, Zhenxia, and Wanli Liu. "A fuzzy rough support vector regression machine." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234232.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Cheng, Gong, and Xiaojun Tong. "Fuzzy Clustering Multiple Kernel Support Vector Machine." In 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2018. http://dx.doi.org/10.1109/icwapr.2018.8521307.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Li, Xuehua, and Lan Shu. "Fuzzy Theory Based Support Vector Machine Classifier." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.440.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sevakula, Rahul K., and Nishchal K. Verma. "Fuzzy Support Vector Machine using Hausdorff distance." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622475.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Xi-zhao, and Shu-xia Lu. "Improved Fuzzy Multicategory Support Vector Machines Classifier." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258575.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hao, Pei-Yi. "Possibilistic regression analysis by support vector machine." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007433.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography