Support-Vector Networks

By Corinna Cortes, Vladimir Naumovich Vapnik. 1995

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Citations in the corpus

Pylearn2: a machine learning research library (2013, background): “Often, LISA researchers work on problems whose scale exceeds that of the typical machine learning library user, so we occasionally implement features for simpler algorithms, such as SVMs [10] with reduced memory consumption or k-means [49] with fast multicore training.”
Masked Autoencoders Are Scalable Vision Learners (2021, background): “It is a common practice to normalize the classiﬁer input when training a classical linear classiﬁer ( e.g ., SVM [11]).”
Challenges in representation learning: A report on three machine learning contests (2013, methodology): “David Thaler won the contest with an accuracy of 70.22% using blending of three models that used sparse ﬁltering[4] for feature learning, random forests for feature selection [5], and support vector machines[6] for classiﬁcation.”
Cascade R-CNN: High Quality Object Detection and Instance Segmentation (2019, background): “, the region of largest margin [5], [14].”
GAN Inversion: A Survey (2021, methodology): “The artifacts generated by PGGAN [11] can be corrected by moving the latent code toward the positive quality direction that is deﬁned by a separating hyperplane using a linear SVM [152].”
Joint Discovery of Object States and Manipulation Actions (2017, methodology): “We then train a linear SVM classifier [9] on bigram text features.”
Optimization Methods for Large-Scale Machine Learning (2016, background): “ always the case. Certain families of functions are amenable to minimization despite having a very large or even innite number of parameters [156, Section 4.11]. For example, support vector machines [38] were designed to take advantage of this fact [156, Theorem 10.3]. All in all, while bounds such ”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Many classical learning algorithms—such as the perceptron (Rosenblatt 1958), support vector machine (SVM) (Cortes and Vapnik 1995), and principal component analysis (PCA) (Pearson 1901, Hotelling 1933)—employ data instances, e.g., x,x′ ∈ X , only through an inner product 〈x,x′〉, which basically is a”
Error Feedback Fixes SignSGD and other Gradient Compression Schemes (2019, background): “Maximizing margin is known to have a regularizing effect and is said to improve generalization Valiant [1984], Cortes and Vapnik [1995].”
Federated learning of predictive models from federated Electronic Health Records (2018, methodology): “We consider SVMs because they are effective classifiers [14] and perform well in predicting hospitalizations.”
Machine Learning Models that Remember Too Much (2017, background): “We then feed the BOW vectors into an SVM or LR model.”
Stealing Hyperparameters in Machine Learning (2018, methodology): “Popular ML algorithms include ridge regression [19], logistic regression [20], SVM [13], and neural network [16].”
Practical Evasion of a Learning-Based Classifier: A Case Study (2014, methodology): “Because of the poor generalization of SVM models in this case, as elaborated in Section VI-B2, the samples are often misclassified.”
Collecting and Analyzing Multidimensional Data with Local Differential Privacy (2019, methodology): “In particular, we focus on three common types of learning tasks: linear regression, logistic regression, and support vector machines (SVM) classification.”
DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, methodology): “We then train a linear Support Vector Machine (SVM) [33] on these features to classify phishing and legitimate pages.”
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017, methodology): “Our Sec-SVM learning algorithm is only an instance that considers the hinge loss and ‘2 regularization, as the standard SVM [18], [41].”