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ICANN
2003
Springer
15 years 9 months ago
Learning Rule Representations from Boolean Data
We discuss a Probably Approximate Correct (PAC) learning paradigm for Boolean formulas, which we call PAC meditation, where the class of formulas to be learnt is not known in advan...
Bruno Apolloni, Andrea Brega, Dario Malchiodi, Gio...
ESANN
2007
15 years 5 months ago
How to process uncertainty in machine learning?
Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
Barbara Hammer, Thomas Villmann
IJCNN
2006
IEEE
15 years 10 months ago
Reinforcement Learning for Parameterized Motor Primitives
Abstract— One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the “building blocks of movement genera...
Jan Peters, Stefan Schaal
BMCBI
2005
122views more  BMCBI 2005»
15 years 4 months ago
A neural strategy for the inference of SH3 domain-peptide interaction specificity
Background: The SH3 domain family is one of the most representative and widely studied cases of so-called Peptide Recognition Modules (PRM). The polyproline II motif PxxP that gen...
Enrico Ferraro, Allegra Via, Gabriele Ausiello, Ma...
VEE
2012
ACM
322views Virtualization» more  VEE 2012»
13 years 11 months ago
Modeling virtualized applications using machine learning techniques
With the growing adoption of virtualized datacenters and cloud hosting services, the allocation and sizing of resources such as CPU, memory, and I/O bandwidth for virtual machines...
Sajib Kundu, Raju Rangaswami, Ajay Gulati, Ming Zh...