Language identification is the task of giving a language label to a text. It is an important preprocessing step in many automatic systems operating with written text. In this paper, we present the evaluation of seven language identification methods that was done in tests between 285 languages with an out-of-domain test set. The evaluated methods are, furthermore, described using unified notatio...

We present a method to automatically segment indoor scenes by detecting repeated objects. Our algorithm scales to datasets with 198 million points and does not require any training data. We propose a trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly-planar patches related by geometric transformations. This representation enables us to robust...

Letter-to-phoneme conversion generally requires aligned training data of letters and phonemes. Typically, the alignments are limited to one-to-one alignments. We present a novel technique of training with many-to-many alignments. A letter chunking bigram prediction manages double letters and double phonemes automatically as opposed to preprocessing with fixed lists. We also apply an HMM method ...

Knowledge discovery is an important process in data analysis, data mining and machine learning. Typically knowledge is presented in the form of rules. However, knowledge discovery systems often generate a huge amount of rules. One of the challenges we face is how to automatically discover interesting and meaningful knowledge from such discovered rules. It is infeasible for human beings to selec...

The similarity assessment process often involves measuring the similarity of objects X and Y in terms of the similarity of corresponding constituents of X and Y, possibly in a recursive manner. This approach is not useful when the verbatim value of the data is of less interest than what they can potentially "do," or where the objects of interest have incomparable representations. We consider th...

In cases of densely occluded urban scenes, it is effective to determine the visibility of scenes, since only small parts of the scene are visible from a given cell. In this paper, we introduce a new visibility preprocessing method that efficiently computes potentially visible objects for volumetric cells. The proposed method deals with general 3D polygonal models and invisible objects jointly b...

Utilizing the advantage of data mining technology in processing large data and eliminating redundant information, the system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features. With this method it can decrease SVM training data and overcome the disadvantage of very lar...

In this paper we describe the problem of routing trains through a railway station. This routing problem is a subproblem of the automatic generation of timetables for the Dutch railway system. The problem of routing trains through a railway station is the problem of assigning each of the involved trains to a route through the railway station, given the detailed layout of the railway network with...

In this paper, we propose a method for generating all the prime implicants of a binary CNF formula. The main idea consists in using an algorithm for generating the maximal independent sets to compute all the primes implicants that cover a model. Such an approach mainly allows to reduce the number of calls to a SAT oracle. Before describing this algorithm, we propose a preprocessing method for r...

We investigate whether kernelization results can be obtained if we restrict kernelization algorithms to run in logarithmic space. This restriction for kernelization is motivated by the question of what results are attainable for preprocessing via simple and/or local reduction rules. We find kernelizations for d-hitting set(k), d-set packing(k), edge dominating set(k) and a number of hitting and...