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ICML
2004
IEEE
15 years 10 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
ECML
2007
Springer
15 years 3 months ago
Avoiding Boosting Overfitting by Removing Confusing Samples
Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorit...
Alexander Vezhnevets, Olga Barinova
ML
2006
ACM
122views Machine Learning» more  ML 2006»
14 years 9 months ago
PRL: A probabilistic relational language
In this paper, we describe the syntax and semantics for a probabilistic relational language (PRL). PRL is a recasting of recent work in Probabilistic Relational Models (PRMs) into ...
Lise Getoor, John Grant
MLDM
2009
Springer
15 years 4 months ago
An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding
This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus a...
Spiros Nikolopoulos, Georgios Th. Papadopoulos, Io...
CONEXT
2006
ACM
15 years 3 months ago
Learning for accurate classification of real-time traffic
Accurate network traffic classification is an important task. We intend to develop an intelligent classification system by learning the types of service inside a network flow usin...
Wei Li, Andrew W. Moore