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CEC
2003
IEEE
14 years 3 months ago
Learning DFA: evolution versus evidence driven state merging
Learning Deterministic Finite Automata (DFA) is a hard task that has been much studied within machine learning and evolutionary computation research. This paper presents a new met...
Simon M. Lucas, T. Jeff Reynolds
KCAP
2003
ACM
14 years 3 months ago
Learning programs from traces using version space algebra
While existing learning techniques can be viewed as inducing programs from examples, most research has focused on rather narrow classes of programs, e.g., decision trees or logic ...
Tessa A. Lau, Pedro Domingos, Daniel S. Weld
HRI
2006
ACM
14 years 4 months ago
Structural descriptions in human-assisted robot visual learning
The paper presents an approach to using structural descriptions, obtained through a human-robot tutoring dialogue, as labels for the visual object models a robot learns. The paper...
Geert-Jan M. Kruijff, John D. Kelleher, Gregor Ber...
VL
2010
IEEE
216views Visual Languages» more  VL 2010»
13 years 8 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
CORR
2008
Springer
107views Education» more  CORR 2008»
13 years 10 months ago
A Spectral Algorithm for Learning Hidden Markov Models
Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical tools for modeling discrete time series. In general, learning HMMs from data is computation...
Daniel Hsu, Sham M. Kakade, Tong Zhang