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» Evaluating learning algorithms and classifiers
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NIPS
2008
15 years 5 months ago
Exact Convex Confidence-Weighted Learning
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that repr...
Koby Crammer, Mark Dredze, Fernando Pereira
VLSISP
2010
254views more  VLSISP 2010»
15 years 2 months ago
Manifold Based Local Classifiers: Linear and Nonlinear Approaches
Abstract In case of insufficient data samples in highdimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nea...
Hakan Cevikalp, Diane Larlus, Marian Neamtu, Bill ...
CVPR
2004
IEEE
16 years 6 months ago
Feature Selection for Classifying High-Dimensional Numerical Data
Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse...
Yimin Wu, Aidong Zhang
KDD
2007
ACM
139views Data Mining» more  KDD 2007»
16 years 4 months ago
Raising the baseline for high-precision text classifiers
Many important application areas of text classifiers demand high precision and it is common to compare prospective solutions to the performance of Naive Bayes. This baseline is us...
Aleksander Kolcz, Wen-tau Yih
DGO
2007
152views Education» more  DGO 2007»
15 years 5 months ago
Identifying and classifying subjective claims
To understand the subjective documents, for example, public comments on the government’s proposed regulation, opinion identification and classification is required. Rather than ...
Namhee Kwon, Liang Zhou, Eduard H. Hovy, Stuart W....