標(biāo)題: Titlebook: Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines; Theory, Algorithms a Jamal Amani Rad,Kourosh Parand,Sneh [打印本頁] 作者: advocate 時間: 2025-3-21 19:54
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines影響因子(影響力)
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines影響因子(影響力)學(xué)科排名
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines網(wǎng)絡(luò)公開度
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines被引頻次
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines被引頻次學(xué)科排名
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書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines年度引用學(xué)科排名
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines讀者反饋
書目名稱Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines讀者反饋學(xué)科排名
作者: 凹槽 時間: 2025-3-21 22:29 作者: ARK 時間: 2025-3-22 04:27 作者: Indigence 時間: 2025-3-22 08:00 作者: adduction 時間: 2025-3-22 11:11 作者: 暫時別動 時間: 2025-3-22 15:12 作者: 胰臟 時間: 2025-3-22 19:06 作者: 全面 時間: 2025-3-23 00:24
Kourosh Parand,Fatemeh Baharifard,Alireza Afzal Aghaei,Mostafa Janinderutilized prism of identity studies. Shibley Telhami and Michael Barnett note that “[a]though scholars of the region cannot escape the salience of identity, a powerful trend in contemporary international relations theory has proceeded as if identity mattered little for our understanding.”. Certai作者: LAP 時間: 2025-3-23 04:16
chiatry” in the future—therapy and coping mechanisms to allow us to form emotionally healthy bonds with the robots that become a key part of r everyday life. It then turns its attention to how ai and humans can actually work together both in the present and future for enhancing their personal growth作者: 失望未來 時間: 2025-3-23 08:04 作者: 偶像 時間: 2025-3-23 13:28 作者: congenial 時間: 2025-3-23 17:20
Sherwin Nedaei Janbesaraei,Amirreza Azmoon,Dumitru Baleanuticularly the elites of society (i.e. the winners in life who have excellent incomes, job statuses and educational qualifications) — the integration project may present countless opportunities to draw upon one’s skills and finances. For the vast majority of Europeans, however, contemplating the spec作者: Confirm 時間: 2025-3-23 20:05 作者: ineluctable 時間: 2025-3-23 23:53 作者: 祖?zhèn)髫敭a(chǎn) 時間: 2025-3-24 03:57 作者: 組裝 時間: 2025-3-24 08:12 作者: 胰島素 時間: 2025-3-24 12:13
Fractional Jacobi Kernel Functions: Theory and Applicationd, the fractional form of Jacobi polynomials will be introduced, and the validation according to Mercer conditions will be proved. Finally, a comparison of the obtained results over a well-known dataset will be provided, using the mentioned kernels with some other orthogonal kernels as well as RBF a作者: 嚴(yán)厲譴責(zé) 時間: 2025-3-24 15:28 作者: 發(fā)牢騷 時間: 2025-3-24 21:45
Book 2023d big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms base作者: 領(lǐng)袖氣質(zhì) 時間: 2025-3-25 03:07
nalysis of positioning strategies in interaction, the book enhances our understanding of the complex possibilities within processes of self-identification in a migration context.978-3-319-81549-7978-3-319-33566-7作者: altruism 時間: 2025-3-25 04:45 作者: 杠桿支點 時間: 2025-3-25 08:21 作者: 鞠躬 時間: 2025-3-25 13:35 作者: 晚來的提名 時間: 2025-3-25 18:50
2364-6837 e Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms base978-981-19-6555-5978-981-19-6553-1Series ISSN 2364-6837 Series E-ISSN 2364-6845 作者: 殺子女者 時間: 2025-3-25 22:47 作者: Ancestor 時間: 2025-3-26 00:24
Amir Hosein Hadian Rasanan,Sherwin Nedaei Janbesaraei,Amirreza Azmoon,Mohammad Akhavan,Jamal Amani R作者: FLIC 時間: 2025-3-26 06:42
Solving Integral Equations by LS-SVRilized for developing a numerical algorithm for solving various types of integral equations. The robustness and also the convergence of the proposed method are discussed in this chapter by providing several numerical examples.作者: thalamus 時間: 2025-3-26 11:25
2364-6837 orthogonal kernels.Contains examples that provide a deep int.This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal 作者: 后來 時間: 2025-3-26 14:01
Book 2023 kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is intro作者: 怪物 時間: 2025-3-26 18:24
Introduction to SVMterpretation is given, and its basic concepts and formulations are described. A history of SVM progress (from Vapnik’s primary works in the 1960s up to now) is also reviewed. Finally, various ML applications of SVM in several fields such as medical, text classification, and image classification are presented.作者: 驚呼 時間: 2025-3-26 23:58
Fractional Chebyshev Kernel Functions: Theory and Applicationd fractional Chebyshev functions, various Chebyshev kernel functions are presented, and fractional Chebyshev kernel functions are introduced. Finally, the performance of the various Chebyshev kernel functions is illustrated on two sample datasets.作者: 真 時間: 2025-3-27 01:46
Fractional Legendre Kernel Functions: Theory and Application some basic features of Legendre and fractional Legendre functions are introduced and reviewed, and then the kernels of these functions are introduced and validated. Finally, the performance of these functions in solving two problems (two sample datasets) is measured.作者: Ostrich 時間: 2025-3-27 05:16
Fractional Gegenbauer Kernel Functions: Theory and?Applicationl properties of Gegenbauer and fractional Gegenbauer functions are presented and reviewed, followed by the kernels of these functions, which are introduced and validated. Finally, the performance of these functions in addressing two issues (two example datasets) is evaluated.作者: abnegate 時間: 2025-3-27 11:01
Classification Using Orthogonal Kernel Functions: Tutorial on?ORSVM Packagech effort to implement. To make it easy for anyone who needs to try and use these kernels, a Python package is provided here. In this chapter, the ORSVM package is introduced as an SVM classification package with orthogonal kernel functions.作者: Mercantile 時間: 2025-3-27 17:36
Solving Ordinary Differential Equations by LS-SVM Finally, by presenting some numerical examples, the results of the current method are compared with other methods. The comparison shows that the proposed method is fast and highly accurate with exponential convergence.作者: cocoon 時間: 2025-3-27 18:47 作者: CYT 時間: 2025-3-28 01:49 作者: COST 時間: 2025-3-28 04:12 作者: PURG 時間: 2025-3-28 07:18
Basics of SVM Method and Least Squares SVM a unique solution and also satisfies the Karush–Kuhn–Tucker conditions, it can be solved very efficiently. In this chapter, the formulation of optimization problems which have arisen in the various forms of support vector machine algorithms is discussed.作者: barium-study 時間: 2025-3-28 12:40
Fractional Chebyshev Kernel Functions: Theory and Applicationgonal functions is producing powerful kernel functions for the support vector machine algorithm. Maybe the simplest orthogonal function that can be used for producing kernel functions is the Chebyshev polynomials. In this chapter, after reviewing some essential properties of Chebyshev polynomials an作者: elastic 時間: 2025-3-28 16:50
Fractional Legendre Kernel Functions: Theory and Applicationctions as a kernel. Linear, radial basis functions, and polynomial functions are the most common functions used in this algorithm. Legendre polynomials are among the most widely used orthogonal polynomials that have achieved excellent results in the support vector machine algorithm. In this chapter,作者: brother 時間: 2025-3-28 21:17
Fractional Gegenbauer Kernel Functions: Theory and?Applicationhine learning issues. Gegenbauer polynomials, like the Chebyshev and Legender polynomials which are introduced in previous chapters, are among the most commonly utilized orthogonal polynomials that have produced outstanding results in the support vector machine method. In this chapter, some essentia作者: 新手 時間: 2025-3-29 02:05 作者: 拍下盜公款 時間: 2025-3-29 04:51
Solving Ordinary Differential Equations by LS-SVMd on the least squares-support vector machines (LS-SVM) with collocation procedure. One of the most important and practical models in this category is Lane-Emden type equations. By using LS-SVM for solving these types of equations, the solution is expanded based on rational Legendre functions and th作者: Lyme-disease 時間: 2025-3-29 10:12 作者: 流眼淚 時間: 2025-3-29 13:53 作者: photophobia 時間: 2025-3-29 19:31 作者: 平常 時間: 2025-3-29 19:51 作者: Intentional 時間: 2025-3-30 02:17 作者: Optic-Disk 時間: 2025-3-30 06:00 作者: 可互換 時間: 2025-3-30 09:37
ers the implications of a prevailing system of beliefs whereThis book uniquely integrates discourse analysis and corpus linguistics to examine representations of the self and other within lifestyle migration. ?With a focus on British migrants living in the Ariège, south-west France, the study identi
