Alexander Gammerman

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In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space. The feature space representation can result in a large increase in the number of parameters used by the algorithm. In order to combat this \curse of(More)
PlantProm DB, a plant promoter database, is an annotated, non-redundant collection of proximal promoter sequences for RNA polymerase II with experimentally determined transcription start site(s), TSS, from various plant species. The first release (2002.01) of PlantProm DB contains 305 entries including 71, 220 and 14 promoters from monocot, dicot and other(More)
We describe a method for predicting a clas­ sification of an object given classifications of the objects in the training set, assuming that the pairs object/classification are generated by an i.i.d. process from a continuous proba­ bility distribution. Our method is a modifica­ tion of Vapnik's support-vector machine; its main novelty is that it gives not(More)
UNLABELLED In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several(More)
Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters. This article describes a new technique for ‘hedging’ the predictions output by many such algorithms, including support vector machines, kernel ridge regression, kernel nearest neighbours, and by many other(More)
In this paper we follow the same general ideology as in [Gammerman et al., 1998], and describe a new transductive learning algorithm using Support Vector Machines. The algorithm presented provides confidence values for its predicted classifications of new examples. We also obtain a measure of “credibility” which serves as an indicator of the reliability of(More)
The existing methods of predicting with conndence give good accuracy and conndence values, but quite often are computationally inee-cient. Some partial solutions have been suggested in the past. Both the original method and these solutions were based on transductive inference. In this paper we make a radical step of replacing transductive inference with(More)
There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a(More)
Most machine learning algorithms share the following drawback: they only output bare predictions but not the conndence in those predictions. In the 1960s algorithmic information theory supplied universal measures of conndence but these are, unfortunately, non-computable. In this paper we combine the ideas of algorithmic information theory with the theory of(More)