References of "Krose, B. J. A"
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See detailSelf-organizing mixture models
Verbeek, J. J.; Vlassis, Nikos UL; Krose, B. J. A.

in Neurocomputing (2005), 63

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have ... [more ▼]

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps (SOMs), the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a penalty term that enforces self-organization. Our approach allows principled handling of missing data and learning of mixtures of SOMs. We present example applications illustrating our approach for continuous, discrete, and mixed discrete and continuous data. (C) 2004 Elsevier B.V. All rights reserved. [less ▲]

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See detailSelf-Organization by Optimizing Free-Energy
Verbeek, J. J.; Vlassis, Nikos UL; Kröse, B. J. A.

in Proc. of European Symposium on Artificial Neural Networks (2003)

We present a variational Expectation-Maximization algorithm to learn probabilistic mixture models. The algorithm is similar to Kohonen's Self-Organizing Map algorithm and not limited to Gaussian mixtures ... [more ▼]

We present a variational Expectation-Maximization algorithm to learn probabilistic mixture models. The algorithm is similar to Kohonen's Self-Organizing Map algorithm and not limited to Gaussian mixtures. We maximize the variational free-energy that sums data log-likelihood and Kullback-Leibler divergence between a normalized neighborhood function and the posterior distribution on the components, given data. We illustrate the algorithm with an application on word clustering. [less ▲]

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See detailA probabilistic model for appearance-based robot localization
Krose, B. J. A.; Vlassis, Nikos UL; Bunschoten, Roland et al

in Image & Vision Computing (2001), 19(6), 381-391

In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image ... [more ▼]

In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image features are extracted using a Principal Component Analysis. The appearance model is represented as a probability density function of the image feature vector given the location of the robot. We estimate this density model from the data with a kernel estimation method. We show how the parameters of the model influence the localization performance. We also study how many features and which features are needed for good localization. (C) 2001 Elsevier Science B.V. All rights reserved. [less ▲]

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