2884 search results - page 223 / 577 » Evaluating learning algorithms and classifiers |
121
Voted
AAAI
2006
15 years 5 months ago
2006
Many learning tasks in adversarial domains tend to be highly dependent on the opponent. Predefined strategies optimized for play against a specific opponent are not likely to succ...
114
Voted
ICML
2008
IEEE
16 years 4 months ago
2008
IEEE
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we pres...
146
Voted
KDD
1999
ACM
15 years 8 months ago
1999
ACM
Satisfying the basic requirements of accuracy and understandability of a classifier, decision tree classifiers have become very popular. Instead of constructing the decision tree ...
151
Voted
PAMI
2007
15 years 3 months ago
2007
Abstract—We experimentally evaluate bagging and seven other randomizationbased approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed o...
136
Voted
NAACL
2010
15 years 1 months ago
2010
Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter updates can be made. SampleRank is a rankbased lear...