This paper presents a new algorithm for clustering. It is an generalisation of the K-means algorithms . Each cluster will be represented by a chain of prototypes instead of being r...
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
In many applications, one is interested to detect certain patterns in random process signals. We consider a class of random process signals which contain sub similarities at rando...
Inspired by recent findings on the similarities between the primary auditory and visual cortex we propose a neural network for speech recognition based on a hierarchical feedforw...
Xavier Domont, Martin Heckmann, Heiko Wersing, Fra...
We discuss the use in machine learning of a general type of convex optimisation problem known as semi-definite programming (SDP) [1]. We intend to argue that SDP’s arise quite n...
Radial basis function network (RBF) kernels are widely used for support vector machines (SVMs). But for model selection of an SVM, we need to optimize the kernel parameter and the ...