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» A Theory for Memory-Based Learning
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NIPS
2007
15 years 7 months ago
Learning Bounds for Domain Adaptation
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classi...
John Blitzer, Koby Crammer, Alex Kulesza, Fernando...
AAAI
2006
15 years 7 months ago
On the Difficulty of Modular Reinforcement Learning for Real-World Partial Programming
In recent years there has been a great deal of interest in "modular reinforcement learning" (MRL). Typically, problems are decomposed into concurrent subgoals, allowing ...
Sooraj Bhat, Charles Lee Isbell Jr., Michael Matea...
NIPS
2001
15 years 7 months ago
Online Learning with Kernels
Abstract--Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is availab...
Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
IJON
2007
117views more  IJON 2007»
15 years 6 months ago
Learning sensory representations with intrinsic plasticity
Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting its intrinsic excitability. Previously, we showed that model neurons combinin...
Nicholas Butko, Jochen Triesch
MANSCI
2007
100views more  MANSCI 2007»
15 years 6 months ago
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Companies such as Zara and World Co. have recently implemented novel product development processes and supply chain architectures enabling them to make more product design and ass...
Felipe Caro, Jérémie Gallien