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» Evaluating learning algorithms and classifiers
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ECML
2007
Springer
14 years 3 months ago
Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks
Abstract. The paper investigates modification of backpropagation algorithm, consisting of discretization of neural network weights after each training cycle. This modification, a...
Marcin Wojnarski
ICDM
2003
IEEE
181views Data Mining» more  ICDM 2003»
14 years 2 months ago
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any online learner for co...
Jeremy Z. Kolter, Marcus A. Maloof
CEAS
2006
Springer
14 years 19 days ago
Fast Uncertainty Sampling for Labeling Large E-mail Corpora
One of the biggest challenges in building effective anti-spam solutions is designing systems to defend against the everevolving bag of tricks spammers use to defeat them. Because ...
Richard Segal, Ted Markowitz, William Arnold
ICDM
2007
IEEE
106views Data Mining» more  ICDM 2007»
14 years 3 months ago
High-Speed Function Approximation
We address a new learning problem where the goal is to build a predictive model that minimizes prediction time (the time taken to make a prediction) subject to a constraint on mod...
Biswanath Panda, Mirek Riedewald, Johannes Gehrke,...
ICAIL
2003
ACM
14 years 2 months ago
Predicting Outcomes of Case-Based Legal Arguments
In this paper, we introduce IBP, an algorithm that combines g with an abstract domain model and case-based reasoning techniques to predict the outcome of case-based legal argument...
Stefanie Brüninghaus, Kevin D. Ashley