Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

This is a book about learning from empirical data (ie, examples, samples, measurements,
records, patterns or observations) by applying support vector machines (SVMs) aka kernel …

This is a book about (machine) learning from (experimental) data. Many books devoted to
this broad field have been published recently. One even feels tempted to begin the previous …

The sampling frequency and quantity of time series data collected from water distribution
systems has been increasing in recent years, giving rise to the potential for improving …

Support vector machine (SVM) soft margin classifiers are important learning algorithms for
classification problems. They can be stated as convex optimization problems and are …

The ramp loss is a robust but non-convex loss for classification. Compared with other non-
convex losses, a local minimum of the ramp loss can be effectively found. The effectiveness …

This paper provides a sparse learning algorithm for Support Vector Classification (SVC),
called Sparse Support Vector Classification (SSVC), which leads to sparse solutions by …

Problems in construction management are complex, full of uncertainty, and vary based on
site environment. Two tools, the fast messy genetic algorithms (fmGA) and support vector …

We introduce a large margin linear binary classification framework that approximates each
class with a hyperdisk–the intersection of the affine support and the bounding hypersphere …

We analyze the computational complexity of Quantum Sparse Support Vector Machine, a
linear classifier that minimizes the hinge loss and the $ L_1 $ norm of the feature weights …