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ICML
2008
IEEE
16 years 7 months ago
Boosting with incomplete information
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we pres...
Feng Jiao, Gholamreza Haffari, Greg Mori, Shaojun ...
GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
16 years 11 days ago
GAMM: genetic algorithms with meta-models for vision
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an ex...
Greg Lee, Vadim Bulitko
ICML
2007
IEEE
16 years 7 months ago
On one method of non-diagonal regularization in sparse Bayesian learning
In the paper we propose a new type of regularization procedure for training sparse Bayesian methods for classification. Transforming Hessian matrix of log-likelihood function to d...
Dmitry Kropotov, Dmitry Vetrov
JMLR
2008
123views more  JMLR 2008»
15 years 6 months ago
Optimization Techniques for Semi-Supervised Support Vector Machines
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs...
Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keer...
ECCV
2006
Springer
16 years 8 months ago
Multivariate Relevance Vector Machines for Tracking
This paper presents a learning based approach to tracking articulated human body motion from a single camera. In order to address the problem of pose ambiguity, a one-to-many mappi...
Arasanathan Thayananthan, Ramanan Navaratnam, Bj&o...