Estimating the error rates of classifiers or regression models is a fundamental task in machine learning which has thus far been studied exclusively using supervised learning tech...
Pinar Donmez, Guy Lebanon, Krishnakumar Balasubram...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
This paper presents an extension of the relevance vector machine (RVM) algorithm to multivariate regression. This allows the application to the task of estimating the pose of an a...
This paper looks at the problem of data prioritization, commonly found in mobile ad-hoc networks. The proposed general solution uses a machine learning approach in order to learn ...