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AAAI
2011
12 years 7 months ago
Fast Newton-CG Method for Batch Learning of Conditional Random Fields
We propose a fast batch learning method for linearchain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dime...
Yuta Tsuboi, Yuya Unno, Hisashi Kashima, Naoaki Ok...
ECAI
2008
Springer
13 years 9 months ago
Learning to Select Object Recognition Methods for Autonomous Mobile Robots
Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and...
Reinaldo A. C. Bianchi, Arnau Ramisa, Ramon L&oacu...
CVPR
2005
IEEE
14 years 9 months ago
A Unified Optimization Based Learning Method for Image Retrieval
In this paper, an optimization based learning method is proposed for image retrieval from graph model point of view. Firstly, image retrieval is formulated as a regularized optimi...
Hanghang Tong, Jingrui He, Mingjing Li, Wei-Ying M...
NIPS
2008
13 years 9 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
KDD
2001
ACM
163views Data Mining» more  KDD 2001»
14 years 8 months ago
Learning to recognize brain specific proteins based on low-level features from on-line prediction servers
During the last decade, the area of bioinformatics has produced an overwhelming amount of data, with the recently published draft of the human genome being the most prominent exam...
Henrik Boström, Joakim Cöster, Lars Aske...