Years and Authors of Summarized Original Work
2005; Ferragina, Giancarlo, Manzini, Sciortino
Problem Definition
Informally, a boosting technique is a method that, when applied to a particular class of algorithms, yields improved algorithms. The improvement must be provable and well defined in terms of one or more of the parameters characterizing the algorithmic performance. Examples of boosters can be found in the context of randomized algorithms (here, a booster allows one to turn a BPP algorithm into an RP one [6]) and computational learning theory (here, a booster allows one to improve the prediction accuracy of a weak learning algorithm [10]). The problem of compression boosting consists of designing a technique that improves the compression performance of a wide class of algorithms. In particular, the results of Ferragina et al. provide a general technique for turning a compressor that uses no context information into one that always uses the best possible context.
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Bell TC, Cleary JG, Witten IH (1990) Text compression. Prentice Hall, Englewood
Burrows M, Wheeler D (1994) A block sorting lossless data compression algorithm. Technical report 124, Digital Equipment Corporation
Ferragina P, Giancarlo R, Manzini G, Sciortino M (2005) Boosting textual compression in optimal linear time. J ACM 52:688–713
Ferragina P, Giancarlo R, Manzini G (2006) The engineering of a compression boosting library: theory vs practice in bwt compression. In: Proceedings of the 14th European symposium on algorithms (ESA), Zurich. LNCS, vol 4168. Springer, Berlin, pp 756–767
Giancarlo R, Restivo A, Sciortino M (2007) From first principles to the Burrows and Wheeler transform and beyond, via combinatorial optimization. Theor Comput Sci 387(3):236–248
Karp R, Pippenger N, Sipser M (1985) A time-randomness tradeoff. In: Proceedings of the conference on probabilistic computational complexity, Santa Barbara. AMS, pp 150–159
Manzini G (2001) An analysis of the Burrows-Wheeler transform. J ACM 48:407–430
Navarro G, Mäkinen V (2007) Compressed full text indexes. ACM Comput Surv 39(1):Article No. 2
Salomon D (2007) Data compression: the complete reference, 4th edn. Springer, New York
Schapire RE (1990) The strength of weak learnability. Mach Learn 2:197–227
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Ferragina, P., Manzini, G. (2016). Boosting Textual Compression. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_54
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DOI: https://doi.org/10.1007/978-1-4939-2864-4_54
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