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ICDM
2005
IEEE
151views Data Mining» more  ICDM 2005»
14 years 3 months ago
A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria
In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...
ICCV
2007
IEEE
14 years 3 months ago
Co-Tracking Using Semi-Supervised Support Vector Machines
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
AAAI
2010
13 years 11 months ago
Cost-Sensitive Semi-Supervised Support Vector Machine
In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequa...
Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou
NIPS
2003
13 years 11 months ago
Semi-Supervised Learning with Trees
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure ...
Charles Kemp, Thomas L. Griffiths, Sean Stromsten,...
EMNLP
2010
13 years 7 months ago
A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations Using Feature Vector Extension
Several recent discourse parsers have employed fully-supervised machine learning approaches. These methods require human annotators to beforehand create an extensive training corp...
Hugo Hernault, Danushka Bollegala, Mitsuru Ishizuk...