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ICML
2010
IEEE
15 years 8 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
JMLR
2006
186views more  JMLR 2006»
15 years 7 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
FTCGV
2011
122views more  FTCGV 2011»
14 years 10 months ago
Structured Learning and Prediction in Computer Vision
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Sebastian Nowozin, Christoph H. Lampert
VL
2007
IEEE
166views Visual Languages» more  VL 2007»
16 years 1 months ago
Community-Based Scaffolding to Promote End-User Learning
End-user programming provides a unique opportunity to study informal computer science education and knowledge acquisition in the real world. We seek to explore the use of communit...
Brian Dorn
PAKDD
2005
ACM
133views Data Mining» more  PAKDD 2005»
16 years 22 days ago
An Anomaly Detection Method for Spacecraft Using Relevance Vector Learning
This paper proposes a novel anomaly detection system for spacecrafts based on data mining techniques. It constructs a nonlinear probabilistic model w.r.t. behavior of a spacecraft ...
Ryohei Fujimaki, Takehisa Yairi, Kazuo Machida