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• W658232237 abstract "The most common approaches to analyzing algorithms are worst-case analysis, average-case analysis, and experimental analysis. In most cases, until recently, experimental analysis has not been given priority. The theoretical computer science community still continues to ignore experimental evaluation, although experimental work dominates algorithmic research in most other areas of computer science and related fields such as operations research. This thesis aims to show the importance of experimental approaches through the solution of three different problems, two in the field of computational geometry and one in the field of statistical machine translation. The GeoFilterKruskal algorithm solves the problem of computing geometric minimum spanning trees (GMST). A common tool in tackling clustering problems, GMSTs are an extension of the minimum spanning tree graph problem, applied to the complete graph of a point set. By using well separated pair decomposition, bi-chromatic closest pair computation, and partitioning and filtering techniques, GeoFilterKruskal greatly reduces the total computation required. It is also one of the only algorithms to compute GMSTs in a manner that lends itself to parallel computation; a major advantage over its competitors. The 1-Center problem is an important application in the field of Geographic Information Systems. Finding exact solution to the problem is computationally expensive. In this thesis, we develop an algorithm that uses computational geometric tools to find an approximate solution to the problem, instantaneously. Exhaustive experiments on real world data sets show that our solution incurs a small constant factor approximation. In the field of Statistical Machine Translation, the Minimum Error Rate Training (MERT) is a widely used algorithm for tuning error parameters. Most of the automated translation algorithms use the classical version of MERT, which fails to capture the underlying distribution of the translation lattice. We propose an algorithm which succeeds in overcoming this drawback, without compromising in the running time both in theory and in practice." @default.
• W658232237 created "2016-06-24" @default.
• W658232237 creator A5027433743 @default.
• W658232237 creator A5040154368 @default.
• W658232237 date "2012-01-01" @default.
• W658232237 modified "2023-09-26" @default.
• W658232237 title "Experimental approaches to computational geometric and statistical machine translation problems" @default.
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