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» Classification with Invariant Distance Substitution Kernels
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GFKL
2007
Springer
164views Data Mining» more  GFKL 2007»
9 years 2 months ago
Classification with Invariant Distance Substitution Kernels
Kernel methods offer a flexible toolbox for pattern analysis and machine learning. A general class of kernel functions which incorporates known pattern invariances are invariant d...
Bernard Haasdonk, Hans Burkhardt
ICPR
2004
IEEE
9 years 11 months ago
Tangent Vector Kernels for Invariant Image Classification with SVMs
This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels base...
Alexei Pozdnoukhov, Samy Bengio
ICPR
2000
IEEE
9 years 11 months ago
Experiments with an Extended Tangent Distance
Invariance is an important aspect in image object recognition. We present results obtained with an extended tangent distance incorporated in a kernel density based Bayesian classi...
Daniel Keysers, Hermann Ney, Jörg Dahmen, Tho...
NIPS
2001
8 years 11 months ago
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms
Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Neares...
Pascal Vincent, Yoshua Bengio
IJON
2006
109views more  IJON 2006»
8 years 10 months ago
Integrating the improved CBP model with kernel SOM
In this paper, we first design a more generalized network model, Improved CBP, based on the same structure as Circular BackPropagation (CBP) proposed by Ridella et al. The novelty ...
Qun Dai, Songcan Chen
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