A platform for massive agent-based simulation and its evaluation
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
Many regression and classification algorithms proposed over the years can be described as greedy procedures for the stagewise minimization of an appropriate cost function. Some examples include additive models, matching pursuit, and boosting. In this work we focus on the classification problem, for which many recent algorithms have been proposed and applied successfully. For a specific regularized form of greedy stagewise optimization, we prove consistency of the approach under rather general conditions. Focusing on specific classes of problems we provide conditions under which our greedy procedure achieves the (nearly) minimax rate of convergence, implying that the procedure cannot be improved in a worst case setting. We also construct a fully adaptive procedure, which, without knowing the smoothness parameter of the decision boundary, converges at the same rate as if the smoothness parameter were known.
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
Hagen Soltau, Lidia Mangu, et al.
ASRU 2011
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Benjamin N. Grosof
AAAI-SS 1993