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ICML
2005
IEEE
14 years 8 months ago
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning
Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference f...
Xiaojin Zhu, John D. Lafferty
NLPRS
2001
Springer
14 years 3 days ago
Named Entity Recognition using Machine Learning Methods and Pattern-Selection Rules
Named Entity recognition, as a task of providing important semantic information, is a critical first step in Information Extraction and QuestionAnswering system. This paper propos...
Choong-Nyoung Seon, Youngjoong Ko, Jeong-Seok Kim,...
ICML
2008
IEEE
14 years 8 months ago
Apprenticeship learning using linear programming
In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in tha...
Umar Syed, Michael H. Bowling, Robert E. Schapire
FGR
2006
IEEE
297views Biometrics» more  FGR 2006»
14 years 1 months ago
Automatic Skin Segmentation for Gesture Recognition Combining Region and Support Vector Machine Active Learning
Skin segmentation is the cornerstone of many applications such as gesture recognition, face detection, and objectionable image filtering. In this paper, we attempt to address the ...
Junwei Han, George Awad, Alistair Sutherland, Hai ...
ECML
2005
Springer
14 years 1 months ago
Combining Bias and Variance Reduction Techniques for Regression Trees
Gradient Boosting and bagging applied to regressors can reduce the error due to bias and variance respectively. Alternatively, Stochastic Gradient Boosting (SGB) and Iterated Baggi...
Yuk Lai Suen, Prem Melville, Raymond J. Mooney