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ABSTRACT: In this work, a new learning paradigm called target selection is proposed, which can be used to test for associations between a single genetic variable and a multidimensional, quantitative phenotype. In target selection, the task of a learning machine is to chose one out of several nominal target variables, as well as a probabilistic classification function for the selected target. For this new paradigm, a cost function is derived from the concept of mutual information and a learning algorithm is suggested. The significance of the generalization performance of the model learned using target selection is tested using a label permutation test. Here, the proposed target selection paradigm is applied to a genomic imaging study.
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on; 01/2009
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International Conference on Machine Learning and Applications, ICMLA 2009, Miami Beach, Florida, USA, December 13-15, 2009; 01/2009
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ABSTRACT: Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of "soft" LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.
Neural Computation 08/2003; 15(7):1589-604. · 1.88 Impact Factor
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ABSTRACT: We consider active data selection and test point rejection strategies for Gaussian process regression based on the variance of the posterior over target values. Gaussian process regression is viewed as transductive regression that provides target distributions for given points rather than selecting an explicit regression function. Since not only the posterior mean but also the posterior variance are easily calculated we use this additional information to two ends: Active data selection is performed by either querying at points of high estimated posterior variance or at points that minimize the estimated posterior variance averaged over the input distribution of interest or --- in a transductive manner --- averaged over the test set. Test point rejection is performed using the estimated posterior variance as a confidence measure. We find for both a two-dimensional toy problem and for a real-world benchmark problem that the variance is a reasonable criterion for both active data...
07/2000;
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ABSTRACT: We consider active data selection and test point rejection
strategies for Gaussian process regression based on the variance of the
posterior over target values. Gaussian process regression is viewed as
transductive regression that provides target distributions for given
points rather than selecting an explicit regression function. Since not
only the posterior mean but also the posterior variance are easily
calculated we use this additional information to two ends: active data
selection is performed by either querying at points of high estimated
posterior variance or at points that minimize the estimated posterior
variance averaged over the input distribution of interest or (in a
transductive manner) averaged over the test set. Test point rejection is
performed using the estimated posterior variance as a confidence
measure. We find that, for both a two-dimensional toy problem and a
real-world benchmark problem, the variance is a reasonable criterion for
both active data selection and test point rejection
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on; 02/2000
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ABSTRACT: In this contribution we present extensions of the Self Organizing Map and clustering methods for the categorization and visualization of data which are described by matrices rather than feature vectors. Rows and Columns of these matrices correspond to objects which may or may not belong to the same set, and the entries in the matrix describe the relationships between them. The clustering task is formulated as an optimization problem: Model complexity is minimized under the constraint, that the error one makes when reconstructing objects from class information is fixed, usually to a small value. The data is then visualized with help of modified Self Organizing Maps methods, i.e. by constructing a neighborhood preserving non-linear projection into a low-dimensional "map-space". Grouping of data objects is done using an improved optimization technique, which combines deterministic annealing with "growing" techniques. Performance of the new methods is evaluated by applying them to two kinds of matrix data: (i) pairwise data, where row and column objects are from the same set and where matrix elements denote dissimilarity values and (ii) co-occurrence data, where row and column objects are from different sets and where the matrix elements describe how often object pairs occur.
Neural Networks 17(8-9):1211-29. · 2.18 Impact Factor
