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» Evaluating learning algorithms and classifiers
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ECML
2004
Springer
15 years 8 months ago
Naive Bayesian Classifiers for Ranking
It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class prob...
Harry Zhang, Jiang Su
CEC
2010
IEEE
15 years 5 months ago
An adaptive ensemble of fuzzy ARTMAP neural networks for video-based face classification
A key feature in population based optimization algorithms is the ability to explore a search space and make a decision based on multiple solutions. In this paper, an incremental le...
Jean-François Connolly, Eric Granger, Rober...
DMIN
2007
203views Data Mining» more  DMIN 2007»
15 years 5 months ago
Evaluation of Feature Selection Techniques for Analysis of Functional MRI and EEG
— The application of feature selection techniques greatly reduces the computational cost of classifying highdimensional data. Feature selection algorithms of varying performance ...
Lauren Burrell, Otis Smart, George J. Georgoulas, ...
ICML
1990
IEEE
15 years 8 months ago
Average Case Analysis of Conjunctive Learning Algorithms
We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to predict the expected accuracy of learning algorithms as a function of the...
Michael J. Pazzani, Wendy Sarrett
KDD
2006
ACM
191views Data Mining» more  KDD 2006»
16 years 4 months ago
Beyond classification and ranking: constrained optimization of the ROI
Classification has been commonly used in many data mining projects in the financial service industry. For instance, to predict collectability of accounts receivable, a binary clas...
Lian Yan, Patrick Baldasare