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» Towards a Combined Approach to Feature Selection
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JSA
2006
97views more  JSA 2006»
13 years 8 months ago
Dynamic feature selection for hardware prediction
It is often possible to greatly improve the performance of a hardware system via the use of predictive (speculative) techniques. For example, the performance of out-of-order micro...
Alan Fern, Robert Givan, Babak Falsafi, T. N. Vija...
PRL
2010
159views more  PRL 2010»
13 years 7 months ago
Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection
The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good res...
Muhammad Atif Tahir, Jim E. Smith
SDM
2009
SIAM
149views Data Mining» more  SDM 2009»
14 years 6 months ago
Near-optimal Supervised Feature Selection among Frequent Subgraphs.
Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and an...
Alexander J. Smola, Arthur Gretton, Hans-Peter Kri...
IJCV
1998
238views more  IJCV 1998»
13 years 8 months ago
Feature Detection with Automatic Scale Selection
The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the not...
Tony Lindeberg
IDA
2007
Springer
14 years 3 months ago
Combining Bagging and Random Subspaces to Create Better Ensembles
Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in b...
Pance Panov, Saso Dzeroski