Log in

Relevant bibliographies by topics / Kernal svm / Journal articles

To see the other types of publications on this topic, follow the link: Kernal svm.

Journal articles on the topic 'Kernal svm'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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 'Kernal svm.'

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

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Yin, Tianzhu. "Quantum support vector machines: theory and applications." Theoretical and Natural Science 51, no. 1 (2024): 34–42. http://dx.doi.org/10.54254/2753-8818/51/2024ch0158.

Full text

Abstract:

Abstract. Quantum Support Vector Machines (QSVMs) combine the fundamental principles of quantum computing and classical Support Vector Machines (SVMs) to improve machine learning performance. In this paper, the author will further explore QSVM. Firstly, introduce the basics of classical SVM, including hyperplane, margin, support vector, and kernel methods. Then, introduce the basic theories of quantum computing, including quantum bits, entanglement, quantum states, superposition, and some related quantum algorithms. Focuses on the concept of QSVM, quantum kernel methods, and how SVM runs on a

APA, Harvard, Vancouver, ISO, and other styles

3

Aziz Firdaus, Abdul, Egi Pratama, Nurul Najwa Sabilla, Rahayu Laras Kinasih, and Wiyanto Wiyanto. "OPTIMASI KINERJA ANALISIS KERNEL SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI OPERASI CAESAR PERSALINAN." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 3 (2025): 5275–82. https://doi.org/10.36040/jati.v9i3.14000.

Full text

Abstract:

Angka persalinan dengan tindakan Sectio Caesarea (SC) di Indonesia terus meningkat dan melebihi batas ideal yang ditetapkan oleh WHO yaitu 10–15%, dengan prevalensi mencapai 17,6%. Hal ini menimbulkan kekhawatiran karena prosedur SC memiliki risiko komplikasi yang lebih tinggi dibandingkan persalinan normal, termasuk infeksi, pendarahan, hingga kematian ibu dan bayi. Meskipun SC menjadi alternatif penting dalam kondisi tertentu, pemilihan metode persalinan yang tidak tepat dapat berdampak negatif terhadap keselamatan ibu dan anak. Keputusan medis yang selama ini cenderung subjektif membutuhkan

APA, Harvard, Vancouver, ISO, and other styles

4

S. Peerbashab, Y. Mohammed Iqbal, Praveen K.P, M. Mohamed Surputheen, and A Saleem Raja. "Diabetes Prediction using Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression Classifiers." JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH 5, no. 4 (2023): 42–54. http://dx.doi.org/10.46947/joaasr542023680.

Full text

Abstract:

One of the world's deadliest diseases is diabetes. It is an additional creator of different assortments of problems. Ex: Coronary disappointment, Visual impairment, Urinary organ illnesses, and so forth. In such cases, the patients are expected to visit a hospital to get a consultation with doctors and their reports. They must contribute their time and cash every time they visit the hospital. Yet, with the development of AI techniques, we have the adaptability to search out a response to the present problem. We have progressed an advanced framework for handling data that can figure regardless

APA, Harvard, Vancouver, ISO, and other styles

5

SS, Sivaraju, Mani V, Sivakumar J, Divya Banu P, Thangarajan S, and Jai Karuna S. "Implementation of Real Time Visual Object Tracking in Clustered Environment using Adaptive Kernal Supported Correlation Filter Algorithm." International Research Journal of Multidisciplinary Scope 05, no. 03 (2024): 1105–18. http://dx.doi.org/10.47857/irjms.2024.v05i03.0953.

Full text

Abstract:

An equivalent design of an SVM model with the expression of the circulant matrix was produced, which served as the basis for the implementation of IVS in this paper. Of the various methods and planned algorithms for tracking objects, we proposed the Scale Adaptive Kernel Support Correlation Filter as an effective method of optimisation for tracking visually. The proposed work was designed to achieve the following goals: produce a video sequence for tracking moving objects; plan an experimental setup for Identifying moving objects; creating and implementing a moving object tracking method. The

APA, Harvard, Vancouver, ISO, and other styles

6

Jain, Pushpam, Amey Deshmukh, and Himanshu Padole. "Design of an Integrated Arrhythmia Detection Model using Connectivity Features and Multivariate Time Series Classification." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 21 (February 20, 2024): 90–98. http://dx.doi.org/10.37394/23209.2024.21.9.

Full text

Abstract:

Cardiac arrhythmia, characterized by irregular heart rhythms, represents a widespread concern within the realm of cardiology. It encompasses a range of rhythm irregularities, with some being benign and others carrying substantial health risks. Therefore, the timely detection of arrhythmia holds considerable importance. Existing methods to detect arrhythmia mainly utilize either the traditional machine learning classifiers like SVM, and random forest or the recent deep learning-based models like CNN, LSTM, and RNN for the classification while few other methods use the classical signal processin

APA, Harvard, Vancouver, ISO, and other styles

7

Mazzia, Vittorio, Aleem Khaliq, and Marcello Chiaberge. "Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)." Applied Sciences 10, no. 1 (2019): 238. http://dx.doi.org/10.3390/app10010238.

Full text

Abstract:

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. E

APA, Harvard, Vancouver, ISO, and other styles

8

Putro, Bagus Prindo Sugihartono, M. Arief Soeleman, and Pujiono Pujiono. "Optimasi Support Vector Machine Dengan PSO Untuk Klasifikasi Kelayakan Export Kerang Batik." Techno.Com 24, no. 1 (2025): 91–103. https://doi.org/10.62411/tc.v24i1.11793.

Full text

Abstract:

Kerang Batik (Paphia undulata) memiliki pola cangkang yang mirip batik, dengan warna dasar cangkang yang bervariasi dari kuning cerah hingga gelap. Sebagai komoditas ekspor Indonesia yang permintaannya terus meningkat, penting untuk menjaga standar kualitas tinggi agar kerang siap ekspor. Penelitian ini menyelidiki metode kontrol kualitas yang efektif untuk kerang batik yang layak ekspor dengan mengambil sampel dari perusahaan terkait. Setelah proses pra-pemrosesan citra, dilakukan ekstraksi fitur, termasuk fitur bentuk (eccentricity, metric) dan fitur tekstur (GLCM). Fitur-fitur ini digunakan

APA, Harvard, Vancouver, ISO, and other styles

9

Lim, Chungsoo. "Adaptive Kernel Function of SVM for Improving Speech/Music Classification of 3GPP2 SMV." ETRI Journal 33, no. 6 (2011): 871–79. http://dx.doi.org/10.4218/etrij.11.0110.0780.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Du, Juan, Wen Long Zhang, and Meng Meng Xie. "Research of a New SVM Kernel Function." Applied Mechanics and Materials 543-547 (March 2014): 1659–62. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1659.

Full text

Abstract:

The kernel was the key technology of SVM; the kernel affected the learning ability and generalization ability of support vector machine. Aiming at the specific application of harmful text information recognition, combining traditional kernel function the paper structured a new combination kernel, modeling for the independent harmful vocabulary and co-occur vocabularies, and then evaluation the linear kernel, homogeneous polynomial kernel, non homogeneous polynomial kernel and combination kernel function in the sample experiment. The experimental results of combination kernel function showed th

APA, Harvard, Vancouver, ISO, and other styles

11

DONG, JIAN-XIONG, CHING Y. SUEN, and ADAM KRZYŻAK. "A FAST SVM TRAINING ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 367–84. http://dx.doi.org/10.1142/s0218001403002423.

Full text

Abstract:

A fast support vector machine (SVM) training algorithm is proposed under SVM's decomposition framework by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Kernel caching plays a key role in reducing the number of kernel evaluations by maximal reusage of cached kernel elements. Extensive experiments have been conducted on a large handwritten digit database MNIST to show that the proposed algorithm is much faster than Keerthi et al.'s improved SMO, about nine times. Combined with principal component analysis, the total training for ten one-against-th

APA, Harvard, Vancouver, ISO, and other styles

12

Gu, Suicheng, and Yuhong Guo. "Learning SVM Classifiers with Indefinite Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (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

APA, Harvard, Vancouver, ISO, and other styles

13

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 (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 vehi

APA, Harvard, Vancouver, ISO, and other styles

14

Rebei, Habib, and Nouf S. H. Alharbi. "Legendre Polynomial Kernel: Application in SVM." Journal of Applied Mathematics and Physics 10, no. 05 (2022): 1732–47. http://dx.doi.org/10.4236/jamp.2022.105121.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Mingqing Hu, Yiqiang Chen, and J. T. Y. Kwok. "Building Sparse Multiple-Kernel SVM Classifiers." IEEE Transactions on Neural Networks 20, no. 5 (2009): 827–39. http://dx.doi.org/10.1109/tnn.2009.2014229.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Shi, Jin Yan, Xue Li, and Yan Xi Li. "Stock Price Index Prediction Based on Improved SVM." Advanced Materials Research 267 (June 2011): 468–71. http://dx.doi.org/10.4028/www.scientific.net/amr.267.468.

Full text

Abstract:

Accurate stock price predicting is a key problem to the financial field. Comparing with the traditional stock price predicting models such as GARCH models and neural networks, the theoretical advantage of applying support vector machine (SVM) to stock price predicting highly depends on solving the problem of kernel function construction and parameter optimization. For the effect of the kernel function in the SVM classification model, a hybrid kernel function is presented. In order to optimize and adjust the important parameters during the process of building the hybrid kernel function, an impr

APA, Harvard, Vancouver, ISO, and other styles

17

Jiang, Hao, and Wai Ki Ching. "Physico-Chemically Weighted Kernel for SVM Protein Classification." Applied Mechanics and Materials 195-196 (August 2012): 385–90. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.385.

Full text

Abstract:

In this paper, a novel kernel taking into consideration of the physico-chemical properties of amino acids as well as the motif information is proposed to tackle the problem of protein classification. Similarity matrix is constructed based on an AAindex2 substitution matrix which measures the amino acid pair distance. Together with the motif content posing importance on the protein sequences, a new kernel is constructed. Numerical examples indicate that the string-based kernel in conjunction with SVM classifier performs significantly better than the traditional spectrum kernel method.

APA, Harvard, Vancouver, ISO, and other styles

18

Maulina Putri, Salma Sheila, Muhammad Arhami, and Hendrawaty Hendrawaty. "Penerapan Metode SVM pada Klasifikasi Kualitas Air." Journal of Artificial Intelligence and Software Engineering (J-AISE) 3, no. 2 (2023): 93. http://dx.doi.org/10.30811/jaise.v3i2.4630.

Full text

Abstract:

SVM adalah salah satu metode learning machine yang bekerja dengan prinsip Structural Risk Minimization (SRM) yang bertujuan untuk menemukan hyperplane terbaik yang memisahkan dua buah class pada input space. Dengan melihat konsep metode SVM yang bekerja dengan menemukan fungsi pemisah optimal yang bisa memisahkan dua set data dari dua kelas yang berbeda. Maka, dari konsep tersebut timbul permasalahan sejauh mana penerapan metode SVM mampu menyelesaikan masalah klasifikasi. Dalam penelitian ini klasifikasi yang dilakukan adalah klasifikasi kualitas air yang akan dinilai berdasarkan WQI (Water Q

APA, Harvard, Vancouver, ISO, and other styles

19

Huanrui, Hao. "New Mixed Kernel Functions of SVM Used in Pattern Recognition." Cybernetics and Information Technologies 16, no. 5 (2016): 5–14. http://dx.doi.org/10.1515/cait-2016-0047.

Full text

Abstract:

Abstract The pattern analysis technology based on kernel methods is a new technology, which combines good performance and strict theory. With support vector machine, pattern analysis is easy and fast. But the existing kernel function fits the requirement. In the paper, we explore the new mixed kernel functions which are mixed with Gaussian and Wavelet function, Gaussian and Polynomial kernel function. With the new mixed kernel functions, we check different parameters. The results shows that the new mixed kernel functions have good time efficiency and accuracy. In image recognition we used SVM

APA, Harvard, Vancouver, ISO, and other styles

20

Zong, Xinlu, Chunzhi Wang, and Hui Xu. "Density-based Adaptive Wavelet Kernel SVM Model for P2P Traffic Classification." International Journal of Future Generation Communication and Networking 6, no. 6 (2013): 25–36. http://dx.doi.org/10.14257/ijfgcn.2013.6.6.04.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Rahayu, Siskawati, and Yuni Yamasari. "Klasifikasi Penyakit Stroke dengan Metode Support Vector Machine (SVM)." Journal of Informatics and Computer Science (JINACS) 5, no. 03 (2024): 440–46. http://dx.doi.org/10.26740/jinacs.v5n03.p440-446.

Full text

Abstract:

Stroke adalah penyakit yang menyerang pada gangguan fungsi syaraf dan menyebabkan gangguan fungsi seperti gangguan penglihatan atau mata, bicara pelo atau cedal, mobilitas terbatas dan kelumpuhan pada wajah atau ekstremitas. Diseluruh dunia, stroke adalah penyakit ketiga yang menyebabkan gangguan syaraf. Untuk itu penelitian ini memfokuskan pada domain tersebut dengan tujuan untuk menemukan model terbaik. Ujicoba dilakukan dengan 14 skenario dan ukuran kinerja model akurasi, presisi dan recall. Selanjutnya, metode yang diterapkan adalah Support Vector Machine dengan kernel linear, polynomial,

APA, Harvard, Vancouver, ISO, and other styles

22

Zhang, Mei Jun, Jie Huang, Kai Chai, and Hao Chen. "Bearing Binary Classification Intelligent Diagnosis by Combined Improved EEMD with SVM." Applied Mechanics and Materials 341-342 (July 2013): 1066–70. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.1066.

Full text

Abstract:

In order to perform the bearing intelligent fault diagnosis,combined improved EEMD with SVM respectively applied to the binary classification identification of bearing normal and ball fault, normal and inner circle fault,normal and outer ring fault in this paper.Improve EEMD decomposed 9d normalized energy for characteristic vector,the SVM binary classification and recognition of bearings normal and ball fault, normal and inner circle fault, normal and outer ring fault is researched.Compared to the SVM classification accuracy using different kernel functions that is linear kernel function, pol

APA, Harvard, Vancouver, ISO, and other styles

23

Wang, Chaoyong, Yanfeng Sun, and Yanchun Liang. "An Improved SVM Based on Similarity Metric." JUCS - Journal of Universal Computer Science 13, no. (10) (2007): 1462–70. https://doi.org/10.3217/jucs-013-10-1462.

Full text

Abstract:

A novel support vector machine method for classification is presented in this paper. A modified kernel function based on the similarity metric and Riemannian metric is applied to the support vector machine. In general, it is believed that the similarity of homogeneous samples is higher than that of inhomogeneous samples. Therefore, in Riemannian geometry, Riemannian metric can be used to reflect local property of a curve. In order to enlarge the similarity metric of the homogeneous samples or reduce that of the inhomogeneous samples in the feature space, Riemannian metric is used in the kernel

APA, Harvard, Vancouver, ISO, and other styles

24

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 info

APA, Harvard, Vancouver, ISO, and other styles

25

Wang, Cheng. "Optimization of SVM Method with RBF Kernel." Applied Mechanics and Materials 496-500 (January 2014): 2306–10. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2306.

Full text

Abstract:

Usually there is no a uniform model to the choice of SVMs kernel function and its parameters for SVM. This paper presents a bilinear grid search method for the purpose of getting the parameter of SVM with RBF kernel, with the approach of combining grid search with bilinear search. Experiment results show that the proposed bilinear grid search has combined both the advantage of moderate training quantity by the bilinear search and of high predict accuracy by the grid search.

APA, Harvard, Vancouver, ISO, and other styles

26

Bai, Yancheng, and Ming Tang. "Robust visual tracking via augmented kernel SVM." Image and Vision Computing 32, no. 8 (2014): 465–75. http://dx.doi.org/10.1016/j.imavis.2014.04.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Czekaj, Tomasz, Wen Wu, and Beata Walczak. "About kernel latent variable approaches and SVM." Journal of Chemometrics 19, no. 5-7 (2005): 341–54. http://dx.doi.org/10.1002/cem.937.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Keerthi, S. S., and S. K. Shevade. "SMO Algorithm for Least-Squares SVM Formulations." Neural Computation 15, no. 2 (2003): 487–507. http://dx.doi.org/10.1162/089976603762553013.

Full text

Abstract:

This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.

APA, Harvard, Vancouver, ISO, and other styles

29

Advait Joshi, Arjun G, Divyam Arora, and Dr. Keerti Kulkarni. "Signature Verification using SVM Classifier." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1213–17. https://doi.org/10.32628/cseit241061164.

Full text

Abstract:

This review examines the use of Support Vector Machine (SVM) classification for automated handwritten signature verification, a vital aspect of biometric authentication and fraud detection. Addressing challenges like intra-class variations and inter-class similarities, the system leverages MATLAB's SVM framework with image preprocessing techniques, including dimensional standardization (100x100 pixels), grayscale conversion, and vector transformation for feature extraction. Using a linear kernel, the SVM effectively constructs optimal hyperplanes to distinguish genuine signatures from forgerie

APA, Harvard, Vancouver, ISO, and other styles

30

Wijaya, Rohmatullah Sony, Arie Qur’ania, and Irma Anggraeni. "Klasifikasi Penyakit Cacar Monyet Menggunakan Support Vector Machine (SVM)." MALCOM: Indonesian Journal of Machine Learning and Computer Science 4, no. 4 (2024): 1253–60. http://dx.doi.org/10.57152/malcom.v4i4.1417.

Full text

Abstract:

Penyakit cacar monyet menjadi sebuah wabah di beberapa negara non endemik pada bulan Juli tahun 2022. Oleh karena itu, tindakan pencegahan atau pengobatan yang tepat perlu dilakukan secara dini dengan cara melakukan identifikasi penyakit menggunakan suatu metode klasifikasi. Klasifikasi dilakukan menggunakan metode knowledge discovery in database (KDD) dengan algoritma support vector machine (SVM) yang menggunakan 4 kernel yaitu linear, RBF, sigmoid, dan polynomial dengan pengaturan parameternya pada masing masing kernel. Algoritma SVM dipilih karena penggunaan berbagai kernelnya memungkinkan

APA, Harvard, Vancouver, ISO, and other styles

31

DIOŞAN, LAURA, ALEXANDRINA ROGOZAN, and JEAN-PIERRE PECUCHET. "LEARNING SVM WITH COMPLEX MULTIPLE KERNELS EVOLVED BY GENETIC PROGRAMMING." International Journal on Artificial Intelligence Tools 19, no. 05 (2010): 647–77. http://dx.doi.org/10.1142/s0218213010000352.

Full text

Abstract:

Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasized the need to consider a combination of kernels — also known as a multiple kernel (MK) — in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that

APA, Harvard, Vancouver, ISO, and other styles

32

Chen, Zhi Ru, Wen Xue Hong, and Pei Pei Zhao. "An Imbalance SVM for MicroRNA Target Genes Prediction." Applied Mechanics and Materials 577 (July 2014): 1245–51. http://dx.doi.org/10.4028/www.scientific.net/amm.577.1245.

Full text

Abstract:

Imbalance miRNA target sample data bring about the lower prediction accuracy of SVM(Support Vector Machine). This paper proposes an SVM algorithm to predict the target genes based on biased discriminant idea. This paper selects an optimal feature sets as input data, and constructs a kernel optimization objective function based on the biased discriminant analysis criteria in the empirical feature space. The conformal transformation of a kernel is utilized to gradually optimize the kernel matrix. Through the comparative analysis of the experimental results of human, mouse and rat, the imbalance

APA, Harvard, Vancouver, ISO, and other styles

33

Wang, Ke, Ligang Cheng, and Bin Yong. "Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification." Remote Sensing 12, no. 13 (2020): 2154. http://dx.doi.org/10.3390/rs12132154.

Full text

Abstract:

Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we

APA, Harvard, Vancouver, ISO, and other styles

34

Mohd Hatta, Noramalina, Zuraini Ali Shah, and Shahreen Kasim. "Evaluate the Performance of SVM Kernel Functions for Multiclass Cancer Classification." International Journal on Data Science 1, no. 1 (2020): 37–41. http://dx.doi.org/10.18517/ijods.1.1.37-41.2020.

Full text

Abstract:

Multiclass cancer classification is basically one of the challenging fields in machine learning which a fast growing technology that use human behaviour as examples. Supervised classification such Support Vector Machine (SVM) has been used to classify the dataset on classification by its own function and merely known as kernel function. Kernel function has stated to have a problem especially in selecting their best kernels based on a specific datasets and tasks. Besides, there is an issue stated that the kernels function have a high impossibility to distribute the data in straight line. Here,

APA, Harvard, Vancouver, ISO, and other styles

35

Wei, Wei, and Qingxuan Jia. "Weighted Feature Gaussian Kernel SVM for Emotion Recognition." Computational Intelligence and Neuroscience 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7696035.

Full text

Abstract:

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Ga

APA, Harvard, Vancouver, ISO, and other styles

36

Ramadhan, Mgs M. Luthfi. "An alternative for kernel SVM when stacked with a neural network." Jurnal Ilmu Komputer dan Informasi 17, no. 1 (2024): 1–7. http://dx.doi.org/10.21609/jiki.v17i1.1172.

Full text

Abstract:

Many studies stack SVM and neural network by utilzing SVM as an output layer of the neural network. However, those studies use kernel before the SVM which is unnecessary. In this study, we proposed an alternative to kernel SVM and proved why kernel is unnecessary when the SVM is stacked on top of neural network. The experiments is done on Dublin City LiDAR data. In this study, we stack PointNet and SVM but instead of using kernel, we simply utilize the last hidden layer of the PointNet. As an alternative to the SVM kernel, this study performs dimension expansion by increasing the number of neu

APA, Harvard, Vancouver, ISO, and other styles

37

Yudha Islami Sulistya, Maie Istighosah, Maryona Septiara, Abednego Dwi Septiadi, and Arif Amrullah. "Classification of Noni Fruit Ripeness Using Support Vector Machine (SVM) Method." Indonesian Journal of Data and Science 5, no. 3 (2024): 206–15. https://doi.org/10.56705/ijodas.v5i3.180.

Full text

Abstract:

The classification of Noni fruit (Morinda citrifolia) ripeness is essential for maximizing its medicinal benefits and ensuring product quality. This research aimed to classify Noni fruit ripeness using the Support Vector Machine (SVM) method, comparing three kernel functions: linear, Radial Basis Function (RBF), and polynomial. A dataset consisting of images of ripe and unripe Noni fruits was utilized, with preprocessing steps including the extraction of color and texture features. Performance evaluation revealed that the RBF kernel achieved the highest accuracy at 86.18%, followed by the poly

APA, Harvard, Vancouver, ISO, and other styles

38

Pamungkas, Daniel S. "Penggunaan Kernel SVM untuk Klasifikasi Pergerakan Jari Mengunakan Sinyal EMG." Jurnal Elektro dan Mesin Terapan (ELEMENTER), Vol. 7 No. 2 (2021) (November 30, 2021): 1–6. http://dx.doi.org/10.35143/elementer.v7i2.5146.

Full text

Abstract:

Biomekanik merupakan sebuah bidang ilmu yang mempelajari pergerakkan makhluk hidup, khususnya manusia. Pada ilmu tersebut dikenal adanya suatu sinyal yang dinamakan electromyography (EMG). EMG adalah suatu sinyal listrik yang berasal dari otot manusia. Sinyal ini banyak digunakan sebagai media pengendali, salah satunya adalah robot tangan. Penelitian ini bertujuan untuk mengklasifikaikan pola pergerakkan jari manusia. Sebuah sensor Myo Armband digunakan untuk mendeteksi sinyal EMG. Sensor ini diletakkan pada lengan bawah tangan kanan subjek untuk mendapatkan sinyal EMG. Sebagian data digunakan

APA, Harvard, Vancouver, ISO, and other styles

39

Dong, Shaojiang, Dihua Sun, Baoping Tang, et al. "Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 15 (2014): 2827–34. http://dx.doi.org/10.1177/0954406214563235.

Full text

Abstract:

In order to effectively recognize the bearing’s running state, a new method based on kernel principal component analysis (KPCA) and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. First, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. Therefore, the nonlinear

APA, Harvard, Vancouver, ISO, and other styles

40

Raharko, Natasha Isnaeni, and Yuni Yamasari. "Klasifikasi Emosi Ulasan Produk E- Commerce Menggunakan Support Vector Machine." Journal of Informatics and Computer Science (JINACS) 6, no. 03 (2024): 606–16. https://doi.org/10.26740/jinacs.v6n03.p606-616.

Full text

Abstract:

Abstrak— Ulasan pada e-commerce dapat mempengaruhi keputusan pembeli dan menunjukkan emosi pembeli terhadap produk yang mereka beli. Namun, analisis terhadap ulasan dalam e-commerce tersebut tidak mudah jika dilakukan secara manual. Oleh karena itu, penelitian ini memfokuskan domain tersebut dengan Support Vector Machine (SVM). Lebih lanjut, penelitian ini mengeksplorasi kernel dari SVM, yaitu: linear, polynomial, RBF, dan sigmoid. Nilai evaluasi tertinggi yang SVM tanpa data resampling untuk accuracy diraih oleh SVM kernel Linear dan Sigmoid sebesar 66.30. Precision, recall, dan f1-score dira

APA, Harvard, Vancouver, ISO, and other styles

41

Gu, Lch, Zhw Ni, and Zhj Wu. "Study of Predictive Method Based on SVM Optimal Model Selection." Applied Mechanics and Materials 65 (June 2011): 443–46. http://dx.doi.org/10.4028/www.scientific.net/amm.65.443.

Full text

Abstract:

The computation time consuming and poor efficiency of prediction exist in the model selection of traditional SVM. By studing on kernel matrix, a SVM-based prediction method for selecting the optimal model framework SVR-D1.2 was proposed with the help of the kernel matrix’s symmetry and positive definition and kernel alignment. The method was applied to the prediction of wheat scab, and comparison experiments were done with the main existing methods. The result shows the method has more efficiency and precision of prediction in the occurrence tendency of wheat scab. Meanwhile, it is simple, pra

APA, Harvard, Vancouver, ISO, and other styles

42

Yao, Yu Kai, Yong Qing Yu, Yang Liu, Jin Jin Wang, and Xiao Yun Chen. "Frequency Hopping Prediction Based on Multi-Kernel SVM." Applied Mechanics and Materials 336-338 (July 2013): 2256–60. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2256.

Full text

Abstract:

The chaotic frequency hopping sequences possesses short-term predictability. Via the phase space reconstruction approach, we can get chaos attractors, and the problem of series prediction can be transformed into the regression problem of the chaotic attractors. This paper uses SVR method to deal with the prediction of frequency hopping sequences. After analyzing the characteristics of existing kernel functions, we produce a new multi-kernel function, which is used for the prediction of frequency hopping sequences. Experiments show the fine performances of our methods.

APA, Harvard, Vancouver, ISO, and other styles

43

Muflikhah, Lailil, and Dimas Joko Haryanto. "High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis." Journal of Information Technology and Computer Science 3, no. 2 (2018): 194. http://dx.doi.org/10.25126/jitecs.20183260.

Full text

Abstract:

Sentiment analysis is a text mining based on the opinion collection towards the review of online product. Support Vector Machine (SVM) is an algorithm of classification that applicable to review the analysis of product. The hyperplane kernel function of SVM has importance role to classify the certain category. Therefore, this research is address to investigate the performance between Polynomial and Radial Basis Function (RBF) kernel functions for sentiment analysis of review product. They are examined to 200 comments using 10-fold validation and various parameter values (learning rate, lambda,

APA, Harvard, Vancouver, ISO, and other styles

44

Xiongwei, Lou, and Huang Decai. "Kernel Selection of SVM for Commerce Image Classification." Research Journal of Applied Sciences, Engineering and Technology 5, no. 20 (2013): 4850–56. http://dx.doi.org/10.19026/rjaset.5.4331.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

YANG, Xu, HuiLin XIONG, and Xin YANG. "Optimal Gaussian Kernel Parameter Selection for SVM Classifier." IEICE Transactions on Information and Systems E93-D, no. 12 (2010): 3352–58. http://dx.doi.org/10.1587/transinf.e93.d.3352.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Qin Jun, Tong Xiaonian, and Yunfei Yi. "Empirical Error-based Kernel Parameters Optimization of SVM." International Journal of Advancements in Computing Technology 5, no. 7 (2013): 852–58. http://dx.doi.org/10.4156/ijact.vol5.issue7.105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Nahar, Jesmin, Shawkat Ali, and Yi-Ping Phoebe Chen. "Microarray Data Classification Using Automatic SVM Kernel Selection." DNA and Cell Biology 26, no. 10 (2007): 707–12. http://dx.doi.org/10.1089/dna.2007.0590.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Jiang, Hao, Wai-Ki Ching, Ka Fai Cedric Yiu, and Yushan Qiu. "Stationary Mahalanobis kernel SVM for credit risk evaluation." Applied Soft Computing 71 (October 2018): 407–17. http://dx.doi.org/10.1016/j.asoc.2018.07.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Xue, Hui, Haiming Xu, Xiaohong Chen, and Yunyun Wang. "A primal perspective for indefinite kernel SVM problem." Frontiers of Computer Science 14, no. 2 (2019): 349–63. http://dx.doi.org/10.1007/s11704-018-8148-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Gong, Xingrui, Bin Zou, Yuze Duan, Jie Xu, Qingxin Luo, and Yan Yang. "Multiple Kernel SVM Based on Two-Stage Learning." IEEE Access 8 (2020): 101133–44. http://dx.doi.org/10.1109/access.2020.2998772.

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!