An Introduction to Support Vector Machines

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  • Hessian scatter regularized twin support vector machine for semi-supervised classification

    2023, Engineering Applications of Artificial Intelligence
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    Support vector machine (SVM), as a classical algorithm in the field of machine learning, has received widespread attention and intensive research in recent decades. It implements the principle of structural risk minimization (SRM) rather than the principle of empirical risk minimization (ERM) (Brown et al., 2000; Sastry, 2002; Cortes and Vapnik, 1995). Based on the solid mathematical theoretical foundation of SVM, many researchers have proposed many excellent SVM variational methods from different perspectives, which have been widely used in many fields (Suykens and Vandewalle, 1999; Mangasarian and Wild, 2006; Jayadeva et al., 2007; Kumar and Gopal, 2009; Shao et al., 2011; Peng and Xu, 2014; Peng, 2010).

  • Detailed analysis of mass transfer in solar food dryer with different methods

    2021, International Communications in Heat and Mass Transfer
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    If the d degree of a polynomial kernel is 1, the kernel will be the same as the linear kernel, and thus hyperplane formation will be easier. However, as the degree increases, the hyperplane will become more complex [39]. Therefore, parameter d is chosen as 1.

  • Abnormal neural activity as a potential biomarker for drug-naive first-episode adolescent-onset schizophrenia with coherence regional homogeneity and support vector machine analyses

    2018, Schizophrenia Research
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    Support vector machine (SVM) is a supervised learning algorithm popular for its four primary factors, namely, strong theoretical foundation, suitable scaling to large datasets, flexibility, and most importantly, accuracy. SVM has been applied in numerous domains, including text categorization, hand-written digital recognition (Schölkop, 2003), and bioinformatics (Ding and Dubchak, 2001; Zien et al., 2000). The algorithm performs discriminative classification and learns by example to predict the classifications of previously unseen data, as well as recognize subtle patterns in complex datasets.

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