Готові списки джерел за темами / Structured Support Vector Machine

Добірка наукової літератури з теми "Structured Support Vector Machine"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Зміст

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Structured Support Vector Machine".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Статті в журналах з теми "Structured Support Vector Machine"

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.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
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.
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
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.
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
Анотація:
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.
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
Анотація:
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.
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
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.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Дисертації з теми "Structured Support Vector Machine"

1

Tsochantaridis, Ioannis. "Support vector machine learning for interdependent and structured output spaces /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174684.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Zhang, Shi-Xiong. "Structured support vector machines for speech recognition." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708040.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Sharma, Siddharth. "Application of Support Vector Machines for Damage Detection in Structures." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/8.

Повний текст джерела
Анотація:
Support vector machines (SVMs) are a set of supervised learning methods that have recently been applied for structural damage detection due to their ability to form an accurate boundary from a small amount of training data. During training, they require data from the undamaged and damaged structure. The unavailability of data from the damaged structure is a major challenge in such methods due to the irreversibility of damage. Recent methods create data for the damaged structure from finite element models. In this thesis we propose a new method to derive the dataset representing the damage structure from the dataset measured on the undamaged structure without using a detailed structural finite element model. The basic idea is to reduce the values of a copy of the data from the undamaged structure to create the data representing the damaged structure. The performance of the method in the presence of measurement noise, ambient base excitation, wind loading is investigated. We find that SVMs can be used to detect small amounts of damage in the structure in the presence of noise. The ability of the method to detect damage at different locations in a structure and the effect of measurement location on the sensitivity of the method has been investigated. An online structural health monitoring method has also been proposed to use the SVM boundary, trained on data measured from the damaged structure, as an indicator of the structural health condition.
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Zhong, Wei. "Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/7.

Повний текст джерела
Анотація:
Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity. Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results. In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied.
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Reyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.

Повний текст джерела
Анотація:
Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Guimarães, Ana Paula Alves [UNESP]. "Utilização do algoritmo de aprendizado de máquinas para monitoramento de falhas em estruturas inteligentes." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/148718.

Повний текст джерела
Анотація:
Submitted by ANA PAULA ALVES GUIMARÃES null (annapaulasun@gmail.com) on 2017-02-04T20:28:04Z No. of bitstreams: 1 dissertação-final.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5)
Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-02-07T13:18:18Z (GMT) No. of bitstreams: 1 guimaraes_apa_me_ilha.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5)
Made available in DSpace on 2017-02-07T13:18:18Z (GMT). No. of bitstreams: 1 guimaraes_apa_me_ilha.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5) Previous issue date: 2016-12-20
O monitoramento da condição estrutural é uma área que vem sendo bastante estudada por permitir a construção de sistemas que possuem a capacidade de identificar um determinado dano em seu estágio inicial, podendo assim evitar sérios prejuízos futuros. O ideal seria que estes sistemas tivessem o mínimo de interferência humana. Sistemas que abordam o conceito de aprendizagem têm a capacidade de serem autômatos. Acredita-se que por possuírem estas propriedades, os algoritmos de aprendizagem de máquina sejam uma excelente opção para realizar as etapas de identificação, localização e avaliação de um dano, com capacidade de obter resultados extremamente precisos e com taxas mínimas de erros. Este trabalho tem como foco principal utilizar o algoritmo support vector machine no auxílio do monitoramento da condição de estruturas e, com isto, obter melhor exatidão na identificação da presença ou ausência do dano, diminuindo as taxas de erros através das abordagens da aprendizagem de máquina, possibilitando, assim, um monitoramento inteligente e eficiente. Foi utilizada a biblioteca LibSVM para análise e validação da proposta. Desta forma, foi possível realizar o treinamento e classificação dos dados promovendo a identificação dos danos e posteriormente, empregando as predições efetuadas pelo algoritmo, foi possível determinar a localização dos danos na estrutura. Os resultados de identificação e localização dos danos foram bastante satisfatórios.
Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Guimarães, Ana Paula Alves. "Utilização do algoritmo de aprendizado de máquinas para monitoramento de falhas em estruturas inteligentes /." Ilha Solteira, 2016. http://hdl.handle.net/11449/148718.

Повний текст джерела
Анотація:
Orientador: Vicente Lopes Junior
Resumo: Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.
Mestre
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Dalvi, Aditi. "Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Altun, Gulsah. "Machine Learning and Graph Theory Approaches for Classification and Prediction of Protein Structure." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/cs_diss/31.

Повний текст джерела
Анотація:
Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers.
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Uziela, Karolis. "Protein Model Quality Assessment : A Machine Learning Approach." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-137695.

Повний текст джерела
Анотація:
Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). These programs are able to predict model accuracy before the native structure is determined. The accuracy estimation can be divided into two parts: global (the whole model accuracy) and local (the accuracy of each residue). ProQ2 is one of the most successful MQAPs for prediction of both local and global model accuracy and is based on a Machine Learning approach. In this thesis, I present my own contribution to Model Quality Assessment (MQA) and the newest developments of ProQ program series. Firstly, I describe a new ProQ2 implementation in the protein modelling software package Rosetta. This new implementation allows use of ProQ2 as a scoring function for conformational sampling inside Rosetta, which was not possible before. Moreover, I present two new methods, ProQ3 and ProQ3D that both outperform their predecessor. ProQ3 introduces new training features that are calculated from Rosetta energy functions and ProQ3D introduces a new machine learning approach based on deep learning. ProQ3 program participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) and was one of the best methods in the MQA category. Finally, an important issue in model quality assessment is how to select a target function that the predictor is trying to learn. In the fourth manuscript, I show that MQA results can be improved by selecting a contact-based target function instead of more conventional superposition based functions.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Книги з теми "Structured Support Vector Machine"

1

Andreas, Christmann, ed. Support vector machines. New York: Springer, 2008.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Joachims, Thorsten. Learning to Classify Text Using Support Vector Machines. Boston, MA: Springer US, 2002.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Campbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

name, No. Least squares support vector machines. Singapore: World Scientific, 2002.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Joachim, Diederich, ed. Rule extraction from support vector machines. Berlin: Springer, 2008.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Hamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Bernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. Cambridge, Mass: MIT Press, 1999.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

K, Suykens Johan A., Signoretto Marco, and Argyriou Andreas, eds. Regularization, optimization, kernels, and support vector machines. Boca Raton: Taylor & Francis, 2014.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Support vector machines for pattern classification. 2nd ed. London: Springer, 2010.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Частини книг з теми "Structured Support Vector Machine"

1

Schiegg, Martin, Ferran Diego, and Fred A. Hamprecht. "Learning Diverse Models: The Coulomb Structured Support Vector Machine." In Computer Vision – ECCV 2016, 585–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46487-9_36.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Joachims, Thorsten. "Structured Output Prediction with Support Vector Machines." In Lecture Notes in Computer Science, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11815921_1.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Hua, Kai-Lung, Irawati Nurmala Sari, and Mei-Chen Yeh. "Human Pose Tracking Using Online Latent Structured Support Vector Machine." In MultiMedia Modeling, 626–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51811-4_51.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Haas, Rainer, and Kurt Pichler. "Fault Diagnosis in a Hydraulic Circuit Using a Support Vector Machine Trained by a Digital Twin." In Advanced Structured Materials, 47–60. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79325-8_5.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Severyn, Aliaksei, and Alessandro Moschitti. "Fast Support Vector Machines for Structural Kernels." In Machine Learning and Knowledge Discovery in Databases, 175–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23808-6_12.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Severyn, Aliaksei, and Alessandro Moschitti. "Large-Scale Support Vector Learning with Structural Kernels." In Machine Learning and Knowledge Discovery in Databases, 229–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15939-8_15.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Le, Hieu Quang, and Stefan Conrad. "Classifying Structured Web Sources Using Support Vector Machine and Aggressive Feature Selection." In Lecture Notes in Business Information Processing, 270–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12436-5_20.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Pan, Hong, Mohsen Azimi, Guoqing Gui, Fei Yan, and Zhibin Lin. "Vibration-Based Support Vector Machine for Structural Health Monitoring." In Lecture Notes in Civil Engineering, 167–78. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67443-8_14.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Schwenker, Friedhelm, and Günther Palm. "Tree-Structured Support Vector Machines for Multi-class Pattern Recognition." In Multiple Classifier Systems, 409–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_41.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Zhang, Zhou Suo, Minghui Shen, Wenzhi Lv, and Zhengjia He. "Multi-Fault Classifier Based on Support Vector Machine and Its Application." In Damage Assessment of Structures VI, 483–92. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-976-8.483.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "Structured Support Vector Machine"

1

Sungwoong Kim, Jongmin Kim, Sungrack Yun, and Chang D. Yoo. "υ-structured support vector machines." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5588703.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Rangkuti, Rizki Perdana, Aprinaldi Jasa Mantau, Vektor Dewanto, Novian Habibie, and Wisnu Jatmiko. "Structured support vector machine learning of conditional random fields." In 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2016. http://dx.doi.org/10.1109/icacsis.2016.7872799.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Li, Yunpeng, and Daniel P. Huttenlocher. "Learning for stereo vision using the structured support vector machine." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587699.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Tsochantaridis, Ioannis, Thomas Hofmann, Thorsten Joachims, and Yasemin Altun. "Support vector machine learning for interdependent and structured output spaces." In Twenty-first international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1015330.1015341.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Zien, Alexander, Ulf Brefeld, and Tobias Scheffer. "Transductive support vector machines for structured variables." In the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273645.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Basudhar, Anirban, and Samy Missoum. "Local Update of Support Vector Machine Decision Boundaries." In 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-2189.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Pei-Yi Hao. "A new fuzzy maximal-margin spherical-structured multi-class support vector machine." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890475.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Yang, J., R. C. van Dalen, S. X. Zhang, and M. J. F. Gales. "Infinite structured support vector machines for speech recognition." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854215.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Tang, Hao, Chao-Hong Meng, and Lin-Shan Lee. "An initial attempt for phoneme recognition using Structured Support Vector Machine (SVM)." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495097.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Lee, Hung-yi, Yu-yu Chou, Yow-Bang Wang, and Lin-shan Lee. "Unsupervised domain adaptation for spoken document summarization with structured support vector machine." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639293.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Звіти організацій з теми "Structured Support Vector Machine"

1

Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.

Повний текст джерела
Анотація:
This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Tabata, Akihisa, and Yoshio Aoki. Application of Support Vector Machines to Structural Health Monitoring. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0102.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Gertz, E. M., and J. D. Griffin. Support vector machine classifiers for large data sets. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/881587.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Alali, Ali. Application of Support Vector Machine in Predicting the Market's Monthly Trend Direction. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1495.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

O'Neill, Francis, Kristofer Lasko, and Elena Sava. Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45842.

Повний текст джерела
Анотація:
This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Arun, Ramaiah, and Shanmugasundaram Singaravelan. Classification of Brain Tumour in Magnetic Resonance Images Using Hybrid Kernel Based Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, October 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Liu, Y. Support vector machine for the prediction of future trend of Athabasca River (Alberta) flow rate. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/299739.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Qi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada458739.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Luo, Yuzhou, Rui Wang, Zhongwei Jiang, and Xiqing Zuo. Assessment of the Effect of Health Monitoring System on the Sleep Quality by Using Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, January 2018. http://dx.doi.org/10.7546/crabs.2018.01.16.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Luo, Yuzhou, Rui Wang, Zhongwei Jiang, and Xiqing Zuo. Assessment of the Effect of Health Monitoring System on the Sleep Quality by Using Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, January 2018. http://dx.doi.org/10.7546/grabs2018.1.16.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Також вас можуть зацікавити розширені добірки на тему "Structured Support Vector Machine" для конкретних типів джерел:
Статті в журналах Дисертації Книги
Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!

До бібліографії