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» Algorithmic Randomness of Closed Sets
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119
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AIME
1997
Springer
15 years 4 months ago
Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods
We used Machine Learning (ML) methods to learn the best decision rules to distinguish normal brain aging from the earliest stages of dementia using subsamples of 198 normal and 244...
William Rodman Shankle, Subramani Mani, Michael J....
110
Voted
PVLDB
2008
138views more  PVLDB 2008»
15 years 1 days ago
A skip-list approach for efficiently processing forecasting queries
Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support pred...
Tingjian Ge, Stanley B. Zdonik
COMPGEOM
2004
ACM
15 years 6 months ago
A 2D kinetic triangulation with near-quadratic topological changes
Given a set of n points S in the plane, a triangulation of S is a subdivision of the convex hull into triangles whose vertices are from S. In the kinetic setting, the input point ...
Pankaj K. Agarwal, Yusu Wang, Hai Yu
85
Voted
PODS
2010
ACM
232views Database» more  PODS 2010»
15 years 5 months ago
Optimal sampling from distributed streams
A fundamental problem in data management is to draw a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With large, streamin...
Graham Cormode, S. Muthukrishnan, Ke Yi, Qin Zhang
118
Voted
BMCBI
2010
198views more  BMCBI 2010»
15 years 20 days ago
ClustalXeed: a GUI-based grid computation version for high performance and terabyte size multiple sequence alignment
Background: There is an increasing demand to assemble and align large-scale biological sequence data sets. The commonly used multiple sequence alignment programs are still limited...
Taeho Kim, Hyun Joo