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ICML
1998
IEEE
14 years 8 months ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...
CONNECTION
2004
98views more  CONNECTION 2004»
13 years 7 months ago
Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting
While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. ...
Bernard Ans, Stephane Rousset, Robert M. French, S...
NIPS
2004
13 years 8 months ago
The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees
Prediction suffix trees (PST) provide a popular and effective tool for tasks such as compression, classification, and language modeling. In this paper we take a decision theoretic...
Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
VISUAL
1999
Springer
13 years 11 months ago
Visual Presentations in Multimedia Learning: Conditions that Overload Visual Working Memory
How should we design visual presentations to explain how a complex system works? One promising approach involves multimedia presentation of explanations in visual and verbal format...
Roxana Moreno, Richard E. Mayer
CORR
2000
Springer
126views Education» more  CORR 2000»
13 years 7 months ago
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
We investigate the performance of two machine learning algorithms in the context of antispam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a nee...
Ion Androutsopoulos, Georgios Paliouras, Vangelis ...