Support vector machine

By William Stafford Noble. 2013

7,171 citations recorded by the source corpus.

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In Statistical Learning Theory

Citations in the corpus

Big Data: Deep Learning for financial sentiment analysis (2018, methodology): “Supervised classification methods, such as Support Vector Machines [48], Naïve Bayes [49] or ensembles [50, 51] have been deployed to perform sentiment analysis in multiple research projects.”
Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam (2019, neutral): “Support vector machine (SVM) is a machine learning algorithm based on the principle of minimizing structural risk to generalize a limited number of samples better and is proposed by Cortes, Vapnik [61].”
Machine Learning for Medical Ultrasound: Status, Methods, and Future Opportunities (2018, background): “The most common approaches applied in the surveyed papers are random forests [42], support vector machines [43, 44], and multilayer feedforward networks [45–47], also known as artificial neural networks.”
Machine Learning in Drug Discovery: A Review (2021, background): “So, it maps multiple features like original space to high dimensional space (Noble 2006).”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Since SVM uses a few support vectors instead of all training samples, the computational complexity reduces significantly and one can avoid the problems of dimensionality.”
Medical hyperspectral imaging: a review (2014, background): “…in two classes, and an indicator vector y ¼ ½ y 1 ; y 2 ; · · · ; y M (cid:1) T ∈ R M such that y i ∈ f 1 ; − 1 g , C-support vector classification 146,147 solves the following primal optimization problem: i ¼ 1 ; : : : ; M: (3) φ ð x i Þ maps x i into a higher-dimensional space and C > 0 is the…”
A Review on a Deep Learning Perspective in Brain Cancer Classification (2019, methodology): “Some of the most common algorithms for classification and segmentation of brain images are: K‐Nearest Neighbors (KNN) [78], Support Vector Machines (SVM) [79], Artificial Neural Networks (ANN) [80] etc.”
Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes (2013, methodology): “To test the predictive power of FKT, the set of transferred and organism-specific annotations were used to train a Support Vector Machine (SVM) classifier [27] and predict new genes to all biological processes in six metazoan organisms.”
Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k‐Nearest Neighbor Approach (2013, methodology): “For further information on the ANN and SVM techniques, see Hecht-Nielsen (1990), Haykin (1994), Vapnik (1995, 1998), and Noble (2006).”
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine (2023, methodology): “If a single hyperplane cannot separate two data clusters, a kernel function can be used to transform the data and achieve a linear separation of the classes [22].”
Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis (2021, methodology): “The machine learning algorithms applied in our project are: • Support Vector Machine (SVM) is a classifier which divides the datasets into classes to find a maximum marginal hyper plane (MMH) via the nearest data points [9].”
Object Detection With Deep Learning: A Review (2018, background): “SPP-net takes almost the same multistage pipeline as R-CNN, including feature extraction, network ﬁne-tuning, SVM training, and bounding-box regressor ﬁtting.”
Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Despite the fact that it can also be applied for regression applications, SVM is a commonly used methodology for classification extending across numerous data science settings [90], including agricultural research.”