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» Learning with the Set Covering Machine
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ML
2002
ACM
123views Machine Learning» more  ML 2002»
13 years 9 months ago
Feature Generation Using General Constructor Functions
Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creatin...
Shaul Markovitch, Dan Rosenstein
ICML
2008
IEEE
14 years 10 months ago
Sparse multiscale gaussian process regression
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
Bernhard Schölkopf, Christian Walder, Kwang I...
ICML
2006
IEEE
14 years 10 months ago
Concept boundary detection for speeding up SVMs
Support Vector Machines (SVMs) suffer from an O(n2 ) training cost, where n denotes the number of training instances. In this paper, we propose an algorithm to select boundary ins...
Navneet Panda, Edward Y. Chang, Gang Wu
ICDM
2010
IEEE
134views Data Mining» more  ICDM 2010»
13 years 7 months ago
Consequences of Variability in Classifier Performance Estimates
The prevailing approach to evaluating classifiers in the machine learning community involves comparing the performance of several algorithms over a series of usually unrelated data...
Troy Raeder, T. Ryan Hoens, Nitesh V. Chawla
PAAMS
2010
Springer
13 years 7 months ago
Variable Length-Based Genetic Representation to Automatically Evolve Wrappers
The Web has been the star service on the Internet, however the outsized information available and its decentralized nature has originated an intrinsic difficulty to locate, extract...
David F. Barrero, Antonio González-Pardo, M...