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» Evaluating learning algorithms and classifiers
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VMV
2008
107views Visualization» more  VMV 2008»
15 years 5 months ago
Learning with Few Examples using a Constrained Gaussian Prior on Randomized Trees
Machine learning with few training examples always leads to over-fitting problems, whereas human individuals are often able to recognize difficult object categories from only one ...
Erik Rodner, Joachim Denzler
ICML
2000
IEEE
16 years 5 months ago
Eligibility Traces for Off-Policy Policy Evaluation
Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference meth...
Doina Precup, Richard S. Sutton, Satinder P. Singh
MCS
2009
Springer
15 years 9 months ago
Incremental Learning of Variable Rate Concept Drift
We have recently introduced an incremental learning algorithm, Learn++ .NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Le...
Ryan Elwell, Robi Polikar
JMLR
2006
123views more  JMLR 2006»
15 years 4 months ago
Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
In this paper, we propose a number of adaptive prototype learning (APL) algorithms. They employ the same algorithmic scheme to determine the number and location of prototypes, but...
Fu Chang, Chin-Chin Lin, Chi-Jen Lu
CIA
2008
Springer
15 years 6 months ago
Trust-Based Classifier Combination for Network Anomaly Detection
Abstract. We present a method that improves the results of network intrusion detection by integration of several anomaly detection algorithms through trust and reputation models. O...
Martin Rehák, Michal Pechoucek, Martin Gril...