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ICML
2009
IEEE
15 years 8 months ago
Active learning for directed exploration of complex systems
Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fid...
Michael C. Burl, Esther Wang
ICML
2007
IEEE
16 years 2 months ago
Learning state-action basis functions for hierarchical MDPs
This paper introduces a new approach to actionvalue function approximation by learning basis functions from a spectral decomposition of the state-action manifold. This paper exten...
Sarah Osentoski, Sridhar Mahadevan
KDD
2004
ACM
330views Data Mining» more  KDD 2004»
16 years 1 months ago
Learning to detect malicious executables in the wild
In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and enc...
Jeremy Z. Kolter, Marcus A. Maloof
GECCO
2009
Springer
110views Optimization» more  GECCO 2009»
15 years 6 months ago
EMO shines a light on the holes of complexity space
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
Núria Macià, Albert Orriols-Puig, Es...
SIGKDD
2010
183views more  SIGKDD 2010»
14 years 8 months ago
Inactive learning?: difficulties employing active learning in practice
Despite the tremendous level of adoption of machine learning techniques in real-world settings, and the large volume of research on active learning, active learning techniques hav...
Josh Attenberg, Foster J. Provost