The nature of statistical learning theory
The nature of statistical learning theory
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
A few notes on statistical learning theory
Advanced lectures on machine learning
The Journal of Machine Learning Research
Evolutionary learning with kernels: a generic solution for large margin problems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Kernel rewards regression: an information efficient batch policy iteration approach
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Invariant kernel functions for pattern analysis and machine learning
Machine Learning
Training SVM with indefinite kernels
Proceedings of the 25th international conference on Machine learning
Grassmann discriminant analysis: a unifying view on subspace-based learning
Proceedings of the 25th international conference on Machine learning
Linear-Time Computation of Similarity Measures for Sequential Data
The Journal of Machine Learning Research
Spectral algorithms for supervised learning
Neural Computation
Learning kernels from indefinite similarities
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Kernels for Periodic Time Series Arising in Astronomy
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Manifold integration with Markov random walks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A Reformulation of Support Vector Machines for General Confidence Functions
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
A neural support vector machine
Neural Networks
Online signature verification with support vector machines based on LCSS kernel functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Efficient approximate Regularized Least Squares by Toeplitz matrix
Pattern Recognition Letters
Geometrical interpretation and applications of membership functions with fuzzy rough sets
Fuzzy Sets and Systems
The dissimilarity space: Bridging structural and statistical pattern recognition
Pattern Recognition Letters
Two new graphs kernels in chemoinformatics
Pattern Recognition Letters
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Full length article: Regularization networks with indefinite kernels
Journal of Approximation Theory
Structure of feature spaces related to fuzzy similarity relations as kernels
Fuzzy Sets and Systems
Approximation and Estimation Bounds for Subsets of Reproducing Kernel Kreĭn Spaces
Neural Processing Letters
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In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that is, kernels which are not positive semidefinite. They do not satisfy Mercer's condition and they induce associated functional spaces called Reproducing Kernel Kre&icaron;n Spaces (RKKS), a generalization of Reproducing Kernel Hilbert Spaces (RKHS).Machine learning in RKKS shares many "nice" properties of learning in RKHS, such as orthogonality and projection. However, since the kernels are indefinite, we can no longer minimize the loss, instead we stabilize it. We show a general representer theorem for constrained stabilization and prove generalization bounds by computing the Rademacher averages of the kernel class. We list several examples of indefinite kernels and investigate regularization methods to solve spline interpolation. Some preliminary experiments with indefinite kernels for spline smoothing are reported for truncated spectral factorization, Landweber-Fridman iterations, and MR-II.