Abstract. Many applications of machine learning involve sparse highdimensional data, where the number of input features is (much) larger than the number of data samples, d n. Predi...
An easily implementable mixed-integer algorithm is proposed that generates a nonlinear kernel support vector machine (SVM) classifier with reduced input space features. A single ...
Existing data mining techniques mostly focus on finding global patterns and lack the ability to systematically discover regional patterns. Most relationships in spatial datasets ar...
Oner Ulvi Celepcikay, Christoph F. Eick, Carlos Or...
Abstract. Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the...
Maximum a posteriori (MAP) inference in graphical models requires that we maximize the sum of two terms: a data-dependent term, encoding the conditional likelihood of a certain la...