Log in

Relevant bibliographies by topics / SVM classification / Journal articles

To see the other types of publications on this topic, follow the link: SVM classification.

Journal articles on the topic 'SVM classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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

Select a source type:

Consult the top 50 journal articles for your research on the topic 'SVM classification.'

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.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Wang, Bo, Yu Kai Yao, Xiao Ping Wang, and Xiao Yun Chen. "PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM." Applied Mechanics and Materials 701-702 (December 2014): 58–62. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.58.

Full text

Abstract:

As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier. This paper proposes a new ensemble algorithm based on SVM. The proposed classification algorithm PB-SVM Ensemble consists of some SVM classifiers produced by PCAenSVM and fifty classifiers trained using Bagging, the results are combined to make the final decision on testing set using majority voting. The performance of PB-SVM Ensemble are evaluated on six datasets which are from UCI repository, Statlog or the famous research. The results of the experiment are compared with LibSVM, PCAenSVM and Bagging. PB-SVM Ensemble outperform other three algorithms in classification accuracy, and at the same time keep a higher confidence of accuracy than Bagging.

APA, Harvard, Vancouver, ISO, and other styles

2

Subha, R., and M. Pushpa Rani. "SVM based Iris Classification." International Journal of Computer Sciences and Engineering 6, no. 2 (February 28, 2018): 321–23. http://dx.doi.org/10.26438/ijcse/v6i2.321323.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Vaidya, Jaideep, Hwanjo Yu, and Xiaoqian Jiang. "Privacy-preserving SVM classification." Knowledge and Information Systems 14, no. 2 (March 24, 2007): 161–78. http://dx.doi.org/10.1007/s10115-007-0073-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Gu, Suicheng, and Yuhong Guo. "Learning SVM Classifiers with Indefinite Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 942–48. http://dx.doi.org/10.1609/aaai.v26i1.8293.

Full text

Abstract:

Recently, training support vector machines with indefinite kernels has attracted great attention in the machine learning community. In this paper, we tackle this problem by formulating a joint optimization model over SVM classifications and kernel principal component analysis. We first reformulate the kernel principal component analysis as a general kernel transformation framework, and then incorporate it into the SVM classification to formulate a joint optimization model. The proposed model has the advantage of making consistent kernel transformations over training and test samples. It can be used for both binary classification and multi-class classification problems. Our experimental results on both synthetic data sets and real world data sets show the proposed model can significantly outperform related approaches.

APA, Harvard, Vancouver, ISO, and other styles

5

Bansal, Esha, and Anupam Bhatia. "Kernel’s Impact on SVM Classification." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (May 30, 2017): 359–62. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0238.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Al-Khalidy, Joanne H., and Raid R. Al-Ne’ma. "Breast Tumor Classification Using SVM." Tikrit Journal of Engineering Sciences 21, no. 1 (July 18, 2013): 43–49. http://dx.doi.org/10.25130/tjes.21.1.06.

Full text

Abstract:

Although there are several techniques that have been used to differentiate between benign andmalignant breast tumor lately, support vector machines (SVMs) have been distinguished as one ofthe common method of classification for many fields such as medical diagnostic, that it offersmany advantages with respect to previously proposed methods such as ANNs. One of them is thatSVM provide a higher accuracy, another advantage that SVM reduces the computational cost,and it is already showed good result in this work.In this paper, a Support Vector Machine for differentiation Breast tumor was presented torecognize malignant or benign in mammograms. This work used 569 cases and they wereclassified into two groups: malignant (+1) or benign (-1), then randomly selected some of thesesamples for training model while others were used for test. The ratios were 84.4.0% of acceptedfalse, 947142% of refused false. These results indicate how much this method is successful.

APA, Harvard, Vancouver, ISO, and other styles

7

Ivanova, Vanya, Tasho Tashev, and Ivo Draganov. "DDoS Attacks Classification using SVM." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 19 (February 9, 2022): 1–11. http://dx.doi.org/10.37394/23209.2022.19.1.

Full text

Abstract:

In this paper two types of classifiers of Distributed Denial of Service (DDoS) attacks, based on Support Vector Machines, are presented – a binary and a multiclass one. They use numerical samples, aggregated from packet switched network connections records, captured between attacking machines, most typically IoT bots and a victim machine. Ten of the most popular DDoS attacks are studied and represented as either 10- or 8-feature vectors. Detection rate and classification accuracy is being measured in both cases, along with lots of other parameters, such as Precision, Recall, F1-measure, training and testing time, and others. Variations with Linear, Polynomial, RBF and Sigmoid kernels are being tried with the SVM. The most accurate turns out to be the RBF SVM, both as detector and multiclass classifier, achieving classification accuracy as high as 0.9999 for some of the attacks. Testing times reveal the practical fitness of the implemented classifiers for real-world application.

APA, Harvard, Vancouver, ISO, and other styles

8

Reynolds, Evan, Brian Callaghan, and Mousumi Banerjee. "SVM–CART for disease classification." Journal of Applied Statistics 46, no. 16 (June 7, 2019): 2987–3007. http://dx.doi.org/10.1080/02664763.2019.1625876.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Sujitha, R., and B. Paramasivan. "Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification." Computers, Materials & Continua 70, no. 1 (2022): 1557–72. http://dx.doi.org/10.32604/cmc.2022.019323.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Huang, Mei-Ling, Yung-Hsiang Hung, W. M. Lee, R. K. Li, and Bo-Ru Jiang. "SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/795624.

Full text

Abstract:

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parametersCandγto increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

APA, Harvard, Vancouver, ISO, and other styles

11

Chao, Chih-Feng, and Ming-Huwi Horng. "The Construction of Support Vector Machine Classifier Using the Firefly Algorithm." Computational Intelligence and Neuroscience 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/212719.

Full text

Abstract:

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

APA, Harvard, Vancouver, ISO, and other styles

12

Karungaru, Stephen, Lyu Dongyang, and Kenji Terada. "Vehicle Detection and Type Classification Based on CNN-SVM." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 304–10. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1052.

Full text

Abstract:

In this paper, we propose vehicle detection and classification in a real road environment using a modified and improved AlexNet. Among the various challenges faced, the problem of poor robustness in extracting vehicle candidate regions through a single feature is solved using the YOLO deep learning series algorithm used to propose potential regions and to further improve the speed of detection. For this, the lightweight network Yolov2-tiny is chosen as the location network. In the training process, anchor box clustering is performed based on the ground truth of the training set, which improves its performance on the specific dataset. The low classification accuracy problem after template-based feature extraction is solved using the optimal feature description extracted through convolution neural network learning. Moreover, based on AlexNet, through adjusting parameters, an improved algorithm was proposed whose model size is smaller and classification speed is faster than the original AlexNet. Spatial Pyramid Pooling (SPP) is added to the vehicle classification network which solves the problem of low accuracy due to image distortion caused by image resizing. By combining CNN with SVM and normalizing features in SVM, the generalization ability of the model was improved. Experiments show that our method has a better performance in vehicle detection and type classification.

APA, Harvard, Vancouver, ISO, and other styles

13

Dewangan, Priyanka, and Vaibhav Dedhe. "Soil Classification Using Image Processing and Modified SVM Classifier." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 504–7. http://dx.doi.org/10.31142/ijtsrd18489.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Roxas, Edison A., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Elmer P. Dadios, and Argel A. Bandala. "SVM Compound Kernel Functions for Vehicle Target Classification." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (September 20, 2018): 654–59. http://dx.doi.org/10.20965/jaciii.2018.p0654.

Full text

Abstract:

The focus of this paper is to explore the use of kernel combinations of the support vector machines (SVMs) for vehicle classification. Being the primary component of the SVM, the kernel functions are responsible for the pattern analysis of the vehicle dataset and to bridge its linear and non-linear features. However, the choice of the type of kernel functions has characteristics and limitations that are highly dependent on the parameters. Thus, in order to overcome these limitations, a method of compounding kernel function for vehicle classification is hereby introduced and discussed. The vehicle classification accuracy of the compound kernel function presented is then compared to the accuracies of the conventional classifications obtained from the four commonly used individual kernel functions (linear, quadratic, cubic, and Gaussian functions). This study provides the following contributions: (1) The classification method is able to determine the rank in terms of accuracies of the four individual kernel functions; (2) The method is able to combine the top three individual kernel functions; and (3) The best combination of the compound kernel functions can be determined.

APA, Harvard, Vancouver, ISO, and other styles

15

Naofal Hakim, Mochamad Agusta, Adiwijaya Adiwijaya, and Widi Astuti. "Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 3393. http://dx.doi.org/10.11591/ijece.v11i4.pp3393-3402.

Full text

Abstract:

Cancer is one of the main causes of death in the world where the World Health Organization (WHO) recognized cancer as among the top causes of death in 2018. Thus, detecting cancer symptoms is paramount in order to cure and subsequently reduce the casualties due to cancer disease. Many studies have been developed data mining approaches to detect symptoms of cancer through a classifying human gene data expression. One popular approach is using microarray data based on DNA. However, DNA microarray data has many dimensions that can have a detrimental effect on the accuracy of classification. Therefore, before performing classification, a feature selection technique must be used to eliminate features that do not have important information to support the classification process. The feature selection techniques used were ReliefF and correlation-based feature selection (CFS) and a classification technique used in this study is support vector machine (SVM). Several testing schemes were applied in this analysis to compare the performance of ReliefF and CFS with SVM. It showed that the ReliefF outperformed compared with CFS as microarray data classification approach.

APA, Harvard, Vancouver, ISO, and other styles

16

Raju, K. Hari Prasada, N. Sandhya, and Raghav Mehra. "Supervised SVM Classification of Rainfall Datasets." Indian Journal of Science and Technology 10, no. 15 (April 1, 2017): 1–6. http://dx.doi.org/10.17485/ijst/2017/v10i15/106115.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Anshori, Mochammad, Wayan Firdaus Mahmudy, and Ahmad Afif Supianto. "Classification Tuberculosis DNA using LDA-SVM." Journal of Information Technology and Computer Science 4, no. 3 (December 20, 2019): 233. http://dx.doi.org/10.25126/jitecs.201943113.

Full text

Abstract:

Tuberculosis is a disease caused by the mycobacterium tuberculosis virus. Tuberculosis is very dangerous and it is included in the top 10 causes of the death in the world. In its detection, errors often occur because it is similar to other diffuse lungs. The challenge is how to better detect using DNA sequence data from mycobacterium tuberculosis. Therefore, preprocessing data is necessary. Preprocessing method is used for feature extraction, it is k-Mer which is then processed again with TF-IDF. The use of dimensional reduction is needed because the data is very large. The used method is LDA. The overall result of this study is the best k value is k = 4 based on the experiment. With performance evaluation accuracy = 0.927, precision = 0.930, recall = 0.927, F score = 0.924, and MCC = 0.875 which obtained from extraction using TF-IDF and dimension reduction using LDA.

APA, Harvard, Vancouver, ISO, and other styles

18

Kulkarni, Atul, and Debajyoti Mukhopadhyay. "SVM Classifier Based Melanoma Image Classification." Research Journal of Pharmacy and Technology 10, no. 12 (2017): 4391. http://dx.doi.org/10.5958/0974-360x.2017.00808.3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Ali, Shawkat, and Kate A. Smith. "Kernal Width Selection for SVM Classification." International Journal of Data Warehousing and Mining 1, no. 4 (October 2005): 78–97. http://dx.doi.org/10.4018/jdwm.2005100104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Vaidehi K. and Manivannan R. "Automated Math Symbol Classification Using SVM." International Journal of e-Collaboration 18, no. 2 (March 1, 2022): 1–14. http://dx.doi.org/10.4018/ijec.304037.

Full text

Abstract:

Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation and features extracted are given into SVM classifier for classification. GLCM and Zernike Moments are the two different feature extraction techniques used in this work. SVM with RBF kernel is used for classification. Zernike Moment features overperforms than GLCM. Zernike Moment achieves 97.89% accuracy and GLCM achieves 87.61% accuracy.

APA, Harvard, Vancouver, ISO, and other styles

21

Maheshwari, Akhil, Dolly Sharma, Ritik Agarwal, Shivam Singh, and Loveleen Kumar. "Gender Classification using SVM With Flask." International Journal of Electrical, Electronics and Computers 6, no. 3 (2021): 43–46. http://dx.doi.org/10.22161/eec.63.6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Reddy, Gaddam Akhil, and Dr B. Indira Reddy. "Classification of Spam Text using SVM." Journal of University of Shanghai for Science and Technology 23, no. 08 (August 17, 2021): 616–24. http://dx.doi.org/10.51201/jusst/21/08437.

Full text

Abstract:

The necessity for spam detection is particularly pertinent nowadays, as there is no quality control over social media, and users have the ability to distribute unverified material, therefore facilitating fraud and deceit. Spam detection can aid in the prevention of such fraud. This scenario has developed mostly as a result of the distribution of disparate, unconfirmed information via shopping websites, emails, and text messages (SMS). There are several ways of categorising and identifying spam. Each of them has certain advantages and disadvantages. The machine learning model “Support Vector Machine” is employed to detect spam in this case. SVM is a basic concept: the method proposes a line or hyperplane to classify the data. The model can categorise any type of text into a given category after being fed a set of labelled training data for each category.

APA, Harvard, Vancouver, ISO, and other styles

23

Jing Peng, D. R. Heisterkamp, and H. K. Dai. "LDA/SVM driven nearest neighbor classification." IEEE Transactions on Neural Networks 14, no. 4 (July 2003): 940–42. http://dx.doi.org/10.1109/tnn.2003.813835.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

K, GOKILA, JANANIE K R, MADHU BALA C, and GOMATHY NAYAGAM M. "Content Based Video Classification Using SVM." Special Issue 5, Special Issue 1 (2019): 468–77. http://dx.doi.org/10.23883/ijrter.conf.20190322.060.iwtaz.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Kim, Daehak, KwangSik Oh, and Jooyong Shim. "On Line LS-SVM for Classification." Communications for Statistical Applications and Methods 10, no. 2 (August 1, 2003): 595–601. http://dx.doi.org/10.5351/ckss.2003.10.2.595.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Huh, Myung-Hoe, and Hee-Man Park. "Visualizing SVM Classification in Reduced Dimensions." Communications for Statistical Applications and Methods 16, no. 5 (September 30, 2009): 881–89. http://dx.doi.org/10.5351/ckss.2009.16.5.881.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Ghosh, Sayantani, and Samir Kumar Bandyopadhyay. "SVM Classifier for Human Gender Classification." International Journal of Applied Research on Information Technology and Computing 7, no. 2 (2016): 100. http://dx.doi.org/10.5958/0975-8089.2016.00010.5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Wang, Qi, Yingjie Tian, and Dalian Liu. "Adaptive FH-SVM for Imbalanced Classification." IEEE Access 7 (2019): 130410–22. http://dx.doi.org/10.1109/access.2019.2940983.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Zhao, Yan Ling, Xiao Shi Zheng, Guang Qi Liu, and Na Li. "Image Texture Classification Based on LS-SVM." Applied Mechanics and Materials 182-183 (June 2012): 869–72. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.869.

Full text

Abstract:

LS-SVM (Least Squares Support Vector Machine) is simple and has a good ability of non-linear regression. As inputs of LS-SVM, DC-Energy-Ratio and Deviation of image samples are extracted first. Output of LS-SVM is the current texture classification. The results show that LS-SVM classifies images accurately by training the proposed two features.

APA, Harvard, Vancouver, ISO, and other styles

30

Shi, Li Jun, Xian Cheng Mao, and Zheng Lin Peng. "Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination." Applied Mechanics and Materials 263-266 (December 2012): 2561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2561.

Full text

Abstract:

This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coefficient was increased by 0.12 compared with SVM classification which has the highest accuracy in single sub-classifications.

APA, Harvard, Vancouver, ISO, and other styles

31

Emilia Ayu Wijayanti, Tania Rahmadanti, and Ultach Enri. "Perbandingan Algoritma SVM dan SVM Berbasis Particle Swarm Optimization Pada Klasifikasi Beras Mekongga." Generation Journal 5, no. 2 (July 21, 2021): 102–8. http://dx.doi.org/10.29407/gj.v5i2.16075.

Full text

Abstract:

Rice is the most important staple food in Indonesia. There are various types of varieties available, one of them is Inpari Mekongga variety. In Karawang, Mekongga rice type is the most popular and superior compared to others. However, this type of rice is often mixed with the other types because there are too many varieties and various other problems. Classifying varieties of rice types can be done to identify the types of rice. The classification of rice varieties in this research is divided into 2 classes, Mekongga and not Mekongga. The method that used in this reserach is Support Vector Machine (SVM) and Particle Swarm Optimatizon (PSO). SVM method was chosen because it basically handles the classification of two classes. Meanwhile, PSO method used to optimize the accuracy level of the SVM method. Combination from the two methods is very well used in classification data because it can increase the level of accuracy better. The purpose of this reserach is compare the accuracy of the 2 methods that used. The results from research is mekongga rice classification with Support Vector Machine has accuracy value 46.67% and AUC value 0.475. Meanwhile, using Support Vector Machine based on Particle Swarm Optimization (PSO) can help improve the classification of this mekongga rice with accuracy value 70.83% and AUC value 0.671.

APA, Harvard, Vancouver, ISO, and other styles

32

R, Vijaya Arjunan. "ECG SIGNAL CLASSIFICATION BASED ON STATISTICAL FEATURES WITH SVM CLASSIFICATION." International Journal of Advances in Signal and Image Sciences 2, no. 1 (June 30, 2016): 5. http://dx.doi.org/10.29284/ijasis.2.1.2016.5-10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Zhang, Rui, Tong Bo Liu, and Ming Wen Zheng. "Semi-Supervised Learning for Classification with Uncertainty." Advanced Materials Research 433-440 (January 2012): 3584–90. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3584.

Full text

Abstract:

Support vector machine (SVM) is a general and powerful learning machine, which adopts supervised manner. However, for many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are very expensive to be obtained. Therefore, semi-supervised learning emerges as the times require. At present, the combination of SVM and semi-supervised learning (S3VM) has attracted more and more attentions. In general, S3VM deals with problems with small training sets and large working sets. When the training set is large relative to the working set, We propose a new SVM model to solve the above classification problem by introducing the fuzzy memberships to each unlabeled point. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.

APA, Harvard, Vancouver, ISO, and other styles

34

Alshamlan, Hala M., Ghada H. Badr, and Yousef A. Alohali. "ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification." International Journal of Machine Learning and Computing 6, no. 3 (June 2016): 184–90. http://dx.doi.org/10.18178/ijmlc.2016.6.3.596.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Ammar Tahir and Adil Pervaiz. "Hand written character recognition using SVM." Pacific International Journal 3, no. 2 (June 30, 2020): 59–62. http://dx.doi.org/10.55014/pij.v3i2.98.

Full text

Abstract:

Classification is one of the most important tasks for different applications such as text categorization, tone recognition, image classification, microarray gene expression, proteins structure predictions, data Classification, etc. Hand-written digit classification is a process that interprets handwritten digits by machine. There are many techniques used for HRC like neural networks and k-nearest neighbor (KNN).In this paper, a novel supervised learning technique, Support Vector Machine (SVM), is applied to blur images data. SVM is a powerful machine model use for classification for two or more classes. This paper represents pixel base detection technique for training machines on blur images. SVM is employed as classifier results are accurate nearest 80% which are comparable with state of art.

APA, Harvard, Vancouver, ISO, and other styles

36

R, Thiruven Gatanadhan. "Speech/music classification using PLP and SVM." International Journal of Engineering and Computer Science 8, no. 02 (February 14, 2019): 24469–72. http://dx.doi.org/10.18535/ijecs.v8i02.4277.

Full text

Abstract:

Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. This paper deals with the Speech/Music classification problem, starting from a set of features extracted directly from audio data. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work Perceptual Linear Prediction (PLP) features are extracted from the input signal. After feature extraction, classification is carried out, using Support Vector Model (SVM) model. The proposed feature extraction and classification models results in better accuracy in speech/music classification.

APA, Harvard, Vancouver, ISO, and other styles

37

Zhang, R., and J. Ma. "An improved SVM method P‐SVM for classification of remotely sensed data." International Journal of Remote Sensing 29, no. 20 (September 20, 2008): 6029–36. http://dx.doi.org/10.1080/01431160802220151.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Nie, Feiping, Wei Zhu, and Xuelong Li. "Decision Tree SVM: An extension of linear SVM for non-linear classification." Neurocomputing 401 (August 2020): 153–59. http://dx.doi.org/10.1016/j.neucom.2019.10.051.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Mohamed, Elfadil Abdalla, Fathi H. Saad, and Omer I. E. Mohamed. "Effects of Classification Techniques on Medical Reports Classification." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 2 (April 16, 2014): 4206–21. http://dx.doi.org/10.24297/ijct.v13i2.2906.

Full text

Abstract:

Text classification is the process of assigning pre-defined category labels to documents based on what a classifications has learned from training examples. This paper investigates the partially supervised classification approach in the medical field. The approaches that have been evaluated include Rocchio, NaÃ¯ve Bayesian (NB), Spy, Support vector machine (SVM), and Expectation Maximization (EM). A combination of these methods has been conducted.Â The experimental result showed that the combination which uses EM in step 2 is always produces better results than those uses SVM using small set of training samples. We also found that reducing the features based on tf-tdf values is decreasing the classification performance dramatically. Moreover, reducing the features based on their frequencies improve the classification performance significantly while also increasing efficiency, but it may require some experimentationÂ

APA, Harvard, Vancouver, ISO, and other styles

40

Dadgostar, Mehrdad, Seyed Kamaledin Setarehdan, Sohrab Shahzadi, and Ata Akin. "CLASSIFICATION OF SCHIZOPHRENIA USING SVM VIA fNIRS." Biomedical Engineering: Applications, Basis and Communications 30, no. 02 (March 26, 2018): 1850008. http://dx.doi.org/10.4015/s1016237218500084.

Full text

Abstract:

In the present study, a classification of functional near-infrared spectroscopy (fNIRS) based on support vector machine (SVM) is presented. It is a non-invasive method monitoring human brain function by evaluating the concentration variation of oxy-hemoglobin and deoxy-hemoglobin. fNIRS is a functional optical imaging technology that measures the neural activities and hemodynamic responses in brain. The data were gathered from 11 healthy volunteers and 16 schizophrenia of the same average age by a 16-channel fNIRS (NIROXCOPE 301 system developed at the Neuro-Optical Imaging Laboratory, continuous-wave dual wavelength). Schizophrenia is a mental disorder that is characterized by mental processing collapse and weak emotional responses. This mental disorder is usually accompanied by a serious disturbance in social and occupational activities. The signals were initially preprocessed by DWT to remove any systemic physiological impediment. A preliminary examination by the genetic algorithm (GA) suggested that some channels of the recreated fNIRS signals required further investigation. The energy of these recreated channel signals was computed and utilized for signal arrangement. We used SVM-based classifier to determine the cases of schizophrenia. The result of six channels is higher than 16 channels. The results demonstrated a classification precision of about 87% in the discovery of schizophrenia in the healthy subjects.

APA, Harvard, Vancouver, ISO, and other styles

41

Tambade, Shital, Madan Somvanshi, Pranjali Chavan, and Swati Shinde. "SVM based Diabetic Classification and Hospital Recommendation." International Journal of Computer Applications 167, no. 1 (June 15, 2017): 40–43. http://dx.doi.org/10.5120/ijca2017914141.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Lee, GeonHui, BaekCheon Shin, and JangWook Hur. "Fault Classification of Gear Pumps Using SVM." Journal of Applied Reliability 20, no. 2 (June 30, 2020): 187–96. http://dx.doi.org/10.33162/jar.2020.6.20.2.187.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Venkatakrishnan, S., V. Ramalingam, and S. Palanivel. "Classification of Oral Submucous Fibrosis using SVM." International Journal of Computer Applications 78, no. 3 (September 18, 2013): 8–11. http://dx.doi.org/10.5120/13467-9311.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

V., Shweta, and Madhu M. "Review: Sentiment Analysis using SVM Classification Approach." International Journal of Computer Applications 181, no. 37 (January 17, 2019): 1–8. http://dx.doi.org/10.5120/ijca2019917993.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Benabdeslem, Khalid, and Younes Bennani. "Dendogram-based SVM for Multi-Class Classification." Journal of Computing and Information Technology 14, no. 4 (2006): 283. http://dx.doi.org/10.2498/cit.2006.04.03.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Chen, Jun Ting, Jian Zhong, Yi Cai Xie, and Cai Yun Cai. "Text Classification Using SVM with Exponential Kernel." Applied Mechanics and Materials 519-520 (February 2014): 807–10. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.807.

Full text

Abstract:

Text classification presents difficult challenges due to the high dimensionality and sparsity of text data, and to the complex semantics of the natural language. Typically, in text classification the documents are represented in the vector space using the Bag of words (BoW) technique. Despite its ease of use, BoW representation does not consider the semantic similarity between words. In this paper, we overcome the shortage of the BoW approach by applying the exponential kernel, which models semantic similarity by means of a diffusion process on a graph defined by lexicon and co-occurrence information, to enrich the BoW representation. Combined with the support vector machine (SVM), experimental evaluation on real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the BoW approach.

APA, Harvard, Vancouver, ISO, and other styles

47

Madhubala, P., and K. Murugesan. "Web Page Classification Using SVM and FURIA." Research Journal of Applied Sciences, Engineering and Technology 9, no. 7 (March 5, 2015): 512–18. http://dx.doi.org/10.19026/rjaset.9.1434.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Subashin, K., S. Palanivel, and V. Ramaligam. "Audio_Video Based Segmentation and Classification Using SVM." Asian Journal of Information Technology 11, no. 1 (January 1, 2012): 30–35. http://dx.doi.org/10.3923/ajit.2012.30.35.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Qian, Chun Hua, He Qun Qiang, and Sheng Rong Gong. "An Image Classification Algorithm Based on SVM." Applied Mechanics and Materials 738-739 (March 2015): 542–45. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.542.

Full text

Abstract:

Image classification is a image processing method which to distinguish between different categories of objectives according to the different features of images. It is widely used in pattern recognition and computer vision. Support Vector Machine (SVM) is a new machine learning method base on statistical learning theory, it has a rigorous mathematical foundation, builts on the structural risk minimization criterion. We design an image classification algorithm based on SVM in this paper, use Gabor wavelet transformation to extract the image feature, use Principal Component Analysis (PCA) to reduce the dimension of feature matrix. We use orange images and LIBSVM software package in our experiments, select RBF as kernel function. The experimetal results demonstrate that the classification accuracy rate of our algorithm beyond 95%.

APA, Harvard, Vancouver, ISO, and other styles

50

Jin, Zhen Lu, Quan Pan, Chun Hui Zhao, and Wen Tian Zhou. "SVM Based Land/Sea Clutter Classification Algorithm." Applied Mechanics and Materials 236-237 (November 2012): 1156–62. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.1156.

Full text

Abstract:

In this paper, a support vector machine (SVM) based land/sea clutter classification algorithm was proposed. For target location error correction based on passive beacon reference source of over-the-horizon radar (OTHR), the signal model of land/sea clutter is established, the three kinds of multi-features of land/sea clutter are analyzed, and the classification algorithm based on SVM using multi-features is detailed. Simulation experiments were carried out for different clutter-noise- ratios, and the results showed that the proposed algorithm has a higher recognition rate of land/sea clutter than algorithms based on single feature of backscatter amplitude or linear discriminant analysis. This paper could provide theoretical guidance for improving target location accuracy.

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!