Fast approximated k-NN classifier
k-th nearest neighbor is a robust data driven classifier. However, the more training samples it uses, the slower it gets in execution. This is because distances from each new observation to all stored training examples (prototypes) need to be computed.
We have developed an approximated k-NN computing distances only to potential nearest neighbors and hence significantly speeding the k-NN execution. Although our strategy to localize the nearest neighbor search is similar to the well-known kd-tree approach, it does not employ per-feature splitting but works directly on distances. Therefore, it scales well to higher dimensionalities unlike the kd-tree which becomes inpractical for more than 20D feature spaces.
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Advanced Patter Recognition Course October 2008
In the end of October the Advanced Pattern Recognition course took place at Delft Technical University. The APRcourse is organized by TUDelft and PR Sys Design and is specifically tailored for people from industry in need of state of the art pattern recognition solutions. There were 12 participants from all over the world (Singapore, Canada and several European countries). This time we invited the participants to briefly introduce themselves and the type of problems they are interested in or dealing with in their work. This gave the opportunity to learn each other interests and background, and stimulated more interactions with the teaching staff and the other participants. A cheerful and cooperative atmosphere developed during the week, boosted also by common dinners and pub celebrations. We have really enjoyed the week and wold like to thank all participants for their contribution. It has been a pleasure to meet and work with you all! Thanks to our “knee man” Shameem we have a group picture of almost the complete team.
LabView interface
We’re happy to announce the LabView interface example graciously donated by Anton Voigt from DeBeers Consolidated Mines. LabView is a an industrial development platform based on graphical language paradigm. It enables fast system integration using multitude of hardware components. The example, which will be available in the coming PRSD Studio release, shows how to call the classifiers trained using PRSD Studio directly from the LabView environment running on PC hardware.
Support for decision trees
We’re happy to announce that PRSD Studio supports execution of decision trees trained in PRTools. Decision tree is a classifier trained feature-per-feature splitting the feature space into rectangular subspaces. The two key advantages of decision trees are interpretation capability (why was the decision made?) and speed. It is the speed of execution that makes decision tree classifier particularly interesting for industrial practitioners!
libPRSD Executing Support Vector classifiers trained in LIBSVM
LIBSVM is a powerful C library implementing Support Vector training and execution with interfaces to numerous scripting environment including Matlab. We’ve posted a Knowledge Base article showing how to bring a classifier, trained in LIBSVM, to the PRSD Studio and through it to custom applications. Executing SVC in libPRSD also brings significant speedup!
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