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ESANN
2004
13 years 10 months ago
Regularizing generalization error estimators: a novel approach to robust model selection
Abstract. A well-known result by Stein shows that regularized estimators with small bias often yield better estimates than unbiased estimators. In this paper, we adapt this spirit ...
Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert M...
MICRO
2007
IEEE
103views Hardware» more  MICRO 2007»
14 years 2 months ago
Mitigating Parameter Variation with Dynamic Fine-Grain Body Biasing
Parameter variation is detrimental to a processor’s frequency and leakage power. One proposed technique to mitigate it is Fine-Grain Body Biasing (FGBB), where different parts o...
Radu Teodorescu, Jun Nakano, Abhishek Tiwari, Jose...
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 9 months ago
Bias and controversy: beyond the statistical deviation
In this paper, we investigate how deviation in evaluation activities may reveal bias on the part of reviewers and controversy on the part of evaluated objects. We focus on a `data...
Hady Wirawan Lauw, Ee-Peng Lim, Ke Wang
JAIR
2000
102views more  JAIR 2000»
13 years 8 months ago
A Model of Inductive Bias Learning
A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem bein...
Jonathan Baxter
ICANN
2007
Springer
14 years 2 months ago
Biasing Neural Networks Towards Exploration or Exploitation Using Neuromodulation
Abstract. Taking neuromodulation as a mechanism underlying emotions, this paper investigates how such a mechanism can bias an artificial neural network towards exploration of new ...
Karla Parussel, Lola Cañamero