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» Learning the kernel via convex optimization
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IJCNN
2007
IEEE
15 years 6 months ago
Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines
Abstract— The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best p...
Gavin C. Cawley, Nicola L. C. Talbot
PKDD
2009
Springer
162views Data Mining» more  PKDD 2009»
15 years 6 months ago
A Convex Method for Locating Regions of Interest with Multi-instance Learning
Abstract. In content-based image retrieval (CBIR) and image screening, it is often desirable to locate the regions of interest (ROI) in the images automatically. This can be accomp...
Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua ...
93
Voted
ICML
2005
IEEE
16 years 14 days ago
Heteroscedastic Gaussian process regression
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance l...
Alexander J. Smola, Quoc V. Le, Stéphane Ca...
ICCV
2009
IEEE
16 years 4 months ago
Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represen...
Samuel Gerber, Tolga Tasdizen, Ross Whitaker
KDD
2005
ACM
109views Data Mining» more  KDD 2005»
16 years 2 days ago
Formulating distance functions via the kernel trick
Tasks of data mining and information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be for...
Gang Wu, Edward Y. Chang, Navneet Panda