The definition of similarity measures—one core component of every CBR application—leads to a serious knowledge acquisition problem if domain and application specific requirem...
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show how to approximat...
Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), e...
Abstract. Gaussian process prior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the...
Abstract. We present an SVM-based learning algorithm for information extraction, including experiments on the influence of different algorithm settings. Our approach needs fewer ...
The informative vector machine (IVM) is a practical method for Gaussian process regression and classification. The IVM produces a sparse approximation to a Gaussian process by com...
Neil D. Lawrence, John C. Platt, Michael I. Jordan
Applications such as audio fingerprinting require search in high dimensions: find an item in a database that is similar to a query. An important property of this search task is t...
Jonathan Goldstein, John C. Platt, Christopher J. ...
Abstract. In recent years the problem of object recognition has received considerable attention from both the machine learning and computer vision communities. The key challenge of...
Abstract. This paper summarizes some of the current research challenges arising from multi-channel sequence processing. Indeed, multiple real life applications involve simultaneous...