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» Semi-Supervised Learning with Trees
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CIDM
2007
IEEE
14 years 3 days ago
Efficient Kernel-based Learning for Trees
Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel ...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...
ECIR
2007
Springer
13 years 9 months ago
Model Tree Learning for Query Term Weighting in Question Answering
Question answering systems rely on retrieval components to identify documents that contain an answer to a user’s question. The formulation of queries that are used for retrieving...
Christof Monz
ICML
2002
IEEE
14 years 9 months ago
Learning Decision Trees Using the Area Under the ROC Curve
ROC analysis is increasingly being recognised as an important tool for evaluation and comparison of classifiers when the operating characteristics (i.e. class distribution and cos...
César Ferri, José Hernández-O...
VMV
2008
107views Visualization» more  VMV 2008»
13 years 9 months ago
Learning with Few Examples using a Constrained Gaussian Prior on Randomized Trees
Machine learning with few training examples always leads to over-fitting problems, whereas human individuals are often able to recognize difficult object categories from only one ...
Erik Rodner, Joachim Denzler
ECCV
2010
Springer
13 years 8 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof