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ICANN
2009
Springer
14 years 5 months ago
Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
Abstract. In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationa...
Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutil...
NECO
2006
103views more  NECO 2006»
13 years 10 months ago
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimi...
Jean-Pascal Pfister, Taro Toyoizumi, David Barber,...
PAA
2010
13 years 8 months ago
Exploiting visual and text features for direct marketing learning in time and space constrained domains
Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audiences. Companies systematically dispatch the offers under consid...
Sebastiano Battiato, Giovanni Maria Farinella, Gio...
COLT
1995
Springer
14 years 1 months ago
Exactly Learning Automata with Small Cover Time
We present algorithms for exactly learning unknown environments that can be described by deterministic nite automata. The learner performs a walk on the target automaton, where at...
Dana Ron, Ronitt Rubinfeld
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 12 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider