TY - GEN
T1 - A simpler analysis of the multi-way branching decision tree boosting algorithm
AU - Hatano, Kohei
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
PY - 2001
Y1 - 2001
N2 - We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. For binary classification problems, the algorithm of Mansour and McAllester constructs a multiway branching decision tree using a set of multi-class hypotheses. Mansour and McAllester proved that it works under certain conditions. We give a much simpler analysis of the algorithm and simplify the conditions. From this simplification, we can provide a simpler algorithm, for which no prior knowledge on the quality of weak hypotheses is necessary.
AB - We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. For binary classification problems, the algorithm of Mansour and McAllester constructs a multiway branching decision tree using a set of multi-class hypotheses. Mansour and McAllester proved that it works under certain conditions. We give a much simpler analysis of the algorithm and simplify the conditions. From this simplification, we can provide a simpler algorithm, for which no prior knowledge on the quality of weak hypotheses is necessary.
UR - https://www.scopus.com/pages/publications/84948154677
UR - https://www.scopus.com/pages/publications/84948154677#tab=citedBy
U2 - 10.1007/3-540-45583-3_8
DO - 10.1007/3-540-45583-3_8
M3 - Conference contribution
AN - SCOPUS:84948154677
SN - 3540428755
SN - 9783540428756
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 92
BT - Algorithmic Learning Theory - 12th International Conference, ALT 2001, Proceedings
A2 - Abe, Naoki
A2 - Khardon, Roni
A2 - Zeugmann, Thomas
PB - Springer Verlag
T2 - 12th Annual Conference on Algorithmic Learning Theory, ALT 2001
Y2 - 25 November 2001 through 28 November 2001
ER -