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ICML
2002
IEEE
15 years 10 months ago
Exact model averaging with naive Bayesian classifiers
The naive classifier is a well-established mathematical model whose simplicity, speed and accuracy have made it a popular choice for classification in AI and engineering. In this ...
Denver Dash, Gregory F. Cooper
MLDM
2009
Springer
15 years 4 months ago
PMCRI: A Parallel Modular Classification Rule Induction Framework
In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction...
Frederic T. Stahl, Max A. Bramer, Mo Adda
ECML
2007
Springer
15 years 3 months ago
Avoiding Boosting Overfitting by Removing Confusing Samples
Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorit...
Alexander Vezhnevets, Olga Barinova
ICML
1994
IEEE
15 years 1 months ago
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the te...
David B. Skalak
TIT
2002
164views more  TIT 2002»
14 years 9 months ago
On the generalization of soft margin algorithms
Generalization bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning syste...
John Shawe-Taylor, Nello Cristianini