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» Evaluating algorithms that learn from data streams
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142
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SC
2003
ACM
15 years 9 months ago
Identifying and Exploiting Spatial Regularity in Data Memory References
The growing processor/memory performance gap causes the performance of many codes to be limited by memory accesses. If known to exist in an application, strided memory accesses fo...
Tushar Mohan, Bronis R. de Supinski, Sally A. McKe...
KDD
2000
ACM
162views Data Mining» more  KDD 2000»
15 years 8 months ago
Data Mining from Functional Brain Images
Recent advances in functional brain imaging enable identication of active areas of a brain performing a certain function. Induction of logical formulas describing relations betwee...
Mitsuru Kakimoto, Chie Morita, Yoshiaki Kikuchi, H...
SODA
2008
ACM
126views Algorithms» more  SODA 2008»
15 years 5 months ago
On distributing symmetric streaming computations
A common approach for dealing with large data sets is to stream over the input in one pass, and perform computations using sublinear resources. For truly massive data sets, howeve...
Jon Feldman, S. Muthukrishnan, Anastasios Sidiropo...
IJCNN
2006
IEEE
15 years 10 months ago
In-Place Learning for Positional and Scale Invariance
— In-place learning is a biologically inspired concept, meaning that the computational network is responsible for its own learning. With in-place learning, there is no need for a...
Juyang Weng, Hong Lu, Tianyu Luwang, Xiangyang Xue
JSS
2008
157views more  JSS 2008»
15 years 4 months ago
Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation
Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can...
Qinbao Song, Martin J. Shepperd, Xiangru Chen, Jun...