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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g.
Cristian Bucila +2 more
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Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g.
Cristian Bucila +2 more
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Stochastic Modeling for Photoplethysmography Compression
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020Photoplethysmography (PPG) has been widely involved in health monitoring for clinical medicine and wearable devices. To make full use of PPG signals for diagnosis and health care, raw PPG waveforms have to be stored and transmitted in a storage and power-efficient way, which is data compression.
Ke Xu 0006 +3 more
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Models for the Compressible Web
2009 50th Annual IEEE Symposium on Foundations of Computer Science, 2009Graphs resulting from human behavior (the web graph, friendship graphs, etc.) have hitherto been viewed as a monolithic class of graphs with similar characteristics; for instance, their degree distributions are markedly heavy-tailed. In this paper we take our understanding of behavioral graphs a step further by showing that an intriguing empirical ...
CHIERICHETTI, FLAVIO +4 more
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ACM Computing Surveys, 1989
The best schemes for text compression use large models to help them predict which characters will come next. The actual next characters are coded with respect to the prediction, resulting in compression of information. Models are best formed adaptively, based on the text seen so far.
Timothy C. Bell +2 more
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The best schemes for text compression use large models to help them predict which characters will come next. The actual next characters are coded with respect to the prediction, resulting in compression of information. Models are best formed adaptively, based on the text seen so far.
Timothy C. Bell +2 more
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Compression by model combination
Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225), 2002In the probabilistic framework for data compression, a model of the probability distribution of a data source is constructed, and the predicted probability is entropy coded. To achieve better compression, most traditional methods resort to higher order models.
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