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Alexander Gammerman
Researcher at Royal Holloway, University of London
Publications - 159
Citations - 6625
Alexander Gammerman is an academic researcher from Royal Holloway, University of London. The author has contributed to research in topics: Probabilistic logic & Support vector machine. The author has an hindex of 36, co-authored 156 publications receiving 6060 citations. Previous affiliations of Alexander Gammerman include Heriot-Watt University & University College London.
Papers
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Proceedings Article
Ridge Regression Learning Algorithm in Dual Variables
TL;DR: A regression estimation algorithm which is a combination of the dual version of Ridge Regression is applied to the ANOVA enhancement of the infinitenode splines and the use of kernel functions, as used in Support Vector methods is introduced.
Book
Algorithmic Learning in a Random World
TL;DR: Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness and describes how several important machine learning problems cannot be solved if the only assumption is randomness.
Proceedings Article
Learning by transduction
TL;DR: In this paper, a method for predicting a classification 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 probability distribution.
Journal ArticleDOI
PlantProm: a database of plant promoter sequences
Ilham A. Shahmuradov,Alexander Gammerman,John M. Hancock,Peter M. Bramley,Victor V. Solovyev +4 more
TL;DR: Analysis of TSS-motifs revealed that their composition is different in dicots and monocots, as well as for TATA and TATA-less promoters, which serves as learning set in developing plant promoter prediction programs.
Book ChapterDOI
Inductive Confidence Machines for Regression
TL;DR: The inductive approach described in this paper may be the only option available when dealing with large data sets and is much faster than the existing transductive techniques.