Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Instance-Based Learning Algorithms
Machine Learning
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
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The problem of identifying cosmic gamma ray events out of charged cosmic ray background (so called hadrons) in Cherenkov telescopes is one of the key problems in VHE gamma ray astronomy. In this contribution, we present a novel approach to this problem by changing the domain representation traditionally used in this field. We have implement different classifiers relying on the information of each pixel of the camera of a Cherenkov telescope, rather than using common Hillas parameter analysis. Separation between gamma-like and hadron-like events is performed using several machine learning techniques, trained using Monte Carlo data samples of both types of events.