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» Convex Programming Methods for Global Optimization
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IJCNN
2006
IEEE
14 years 1 months ago
Learning the Kernel in Mahalanobis One-Class Support Vector Machines
— In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function ...
Ivor W. Tsang, James T. Kwok, Shutao Li
TSP
2010
13 years 2 months ago
A dual perspective on separable semidefinite programming with applications to optimal downlink beamforming
This paper considers the downlink beamforming optimization problem that minimizes the total transmission power subject to global shaping constraints and individual shaping constrai...
Yongwei Huang, Daniel Pérez Palomar
ICML
2009
IEEE
14 years 8 months ago
Learning kernels from indefinite similarities
Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similariti...
Yihua Chen, Maya R. Gupta, Benjamin Recht
CEC
2007
IEEE
13 years 9 months ago
Hybrid optimization using DIRECT, GA, and SQP for global exploration
— As there are many good optimization algorithms each with its own characteristics, it is very difficult to choose the best method for optimization problems. Thus, it is importa...
Satoru Hiwa, Tomoyuki Hiroyasu, Mitsunori Miki
NIPS
2007
13 years 9 months ago
Convex Clustering with Exemplar-Based Models
Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization pro...
Danial Lashkari, Polina Golland