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» Evaluating algorithms that learn from data streams
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149
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MOBISYS
2009
ACM
16 years 4 months ago
Sensor selection for energy-efficient ambulatory medical monitoring
Epilepsy affects over three million Americans of all ages. Despite recent advances, more than 20% of individuals with epilepsy never achieve adequate control of their seizures. Th...
Eugene Shih, Ali H. Shoeb, John V. Guttag
180
Voted
CHI
2011
ACM
14 years 7 months ago
Apolo: making sense of large network data by combining rich user interaction and machine learning
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduc...
Duen Horng Chau, Aniket Kittur, Jason I. Hong, Chr...
PCI
2005
Springer
15 years 9 months ago
Protein Classification with Multiple Algorithms
Nowadays, the number of protein sequences being stored in central protein databases from labs all over the world is constantly increasing. From these proteins only a fraction has b...
Sotiris Diplaris, Grigorios Tsoumakas, Pericles A....
139
Voted
PAKDD
2007
ACM
144views Data Mining» more  PAKDD 2007»
15 years 10 months ago
Approximately Mining Recently Representative Patterns on Data Streams
Catching the recent trend of data is an important issue when mining frequent itemsets from data streams. To prevent from storing the whole transaction data within the sliding windo...
Jia-Ling Koh, Yuan-Bin Don
218
Voted
ICDE
2005
IEEE
106views Database» more  ICDE 2005»
16 years 5 months ago
Effective Computation of Biased Quantiles over Data Streams
Skewis prevalentin manydata sourcessuchas IP traffic streams. To continually summarize the distribution of such data, a highbiased set of quantiles (e.g., 50th, 90th and 99th perc...
Graham Cormode, Flip Korn, S. Muthukrishnan, Dives...