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Design of Experiments and Its Deployment in SAS and R
Published in Tanya Kolosova, Samuel Berestizhevsky, Supervised Machine Learning, 2020
Tanya Kolosova, Samuel Berestizhevsky
A factor of an experiment is a controlled independent variable. The values (or levels) of such a variable are set by the experimenter. In the AI framework, a degree of polynomial kernel is one of the factors that can have values on three levels such as 2, 3, or 4. Another example of a factor is the cost variable that can be defined on three levels. For example, level 1 means that the value of the cost variable belongs to an interval [1; 5), level 2 means that the value of the cost variable belongs to an interval [5; 20), and level 3 is linked with an interval [20, 100).
Power Amplifier Behavioral Model and Nonlinear Analysis Basis
Published in Jingchang Nan, Mingming Gao, Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers, 2021
According to the related functional theory, the symmetric functions meeting the mercer condition are considered kernel functions. At present, the kernel functions that are most applied and investigated in SVM are q-order polynomial kernel function, radial basis function and sigmoid function.
Arrhythmia detection by extracting hybrid features based on refined Fuzzy entropy (FuzEn) approach and employing machine learning techniques
Published in Waves in Random and Complex Media, 2020
Lal Hussain, Wajid Aziz, Sharjil Saeed, Imtiaz Ahmed Awan, Adeel Ahmed Abbasi, Neelum Maroof
SVM Polynomial Kernel SVM Gaussian (RBF) kernel SVM Fine Gaussian (RBF) kernel Where n is the order of polynomial kernel and is the width of RBF. The dual formulation for non-linear case is given by Subject to The SVM classifier performance depends on several parameters. The grid search method (Huang et al, 2006) was used to select the optimal parameter value by carefully setting grid range and step size. The linear kernel involves only one parameter (‘c’ soft margin constant), that represent the constraint violation cost associated with the data point occurring on the wrong side of the decision surface. The SVM with RBF and Gaussian kernel function has two training parameters: cost (C) which control the overfitting of the model and sigma (), which control the degree of nonlinearity of the model. The default values of cost function and sigma are used.
Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model
Published in Engineering Applications of Computational Fluid Mechanics, 2020
Anurag Malik, Anil Kumar, Sungwon Kim, Mahsa H. Kashani, Vahid Karimi, Ahmad Sharafati, Mohammad Ali Ghorbani, Nadhir Al-Ansari, Sinan Q. Salih, Zaher Mundher Yaseen, Kwok-Wing Chau
The SVM is mainly useful in regression and classification tasks. The model performs using the concepts of structural risk minimization (SRM) and traditional empirical risk minimization (ERM). Both concepts are normally applied by conventional neural networks (CNN). All the input space operations in the potentially low-dimensional feature space are performed by the kernel function in SVM. The more information about the SVM and its applications can be found in some studies such as reference evapotranspiration modeling (Kişi & Cimen, 2009), pan evaporation simulation (Goyal et al., 2014), and drought modeling (Deo et al., 2017). In this research, the SVM model was conducted by considering a gamma regularization parameter value of 0.0064 and polynomial kernel function with 3rd degree.
Support vector machine (SVM) classification of cognitive tasks based on electroencephalography (EEG) engagement index
Published in Brain-Computer Interfaces, 2018
The kernel function, given by (10), is a dot product in some feature space. Four basic kernels used in SVM research are the linear kernel, the polynomial kernel, the radial basis function (RBF) kernel, and the sigmoid kernel.