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» Hedging predictions in machine learning
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BMCBI
2007
121views more  BMCBI 2007»
15 years 6 months ago
Predicting zinc binding at the proteome level
Background: Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for ...
Andrea Passerini, Claudia Andreini, Sauro Menchett...
ICASSP
2011
IEEE
14 years 10 months ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...
ICML
2009
IEEE
16 years 7 months ago
Proximal regularization for online and batch learning
Many learning algorithms rely on the curvature (in particular, strong convexity) of regularized objective functions to provide good theoretical performance guarantees. In practice...
Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo
ICML
2003
IEEE
16 years 7 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
ML
2008
ACM
15 years 6 months ago
Margin-based first-order rule learning
Abstract We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the comp...
Ulrich Rückert, Stefan Kramer