Robust regression and outlier detection
Robust regression and outlier detection
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Metamodel-Assisted Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Principal Surfaces from Unsupervised Kernel Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Evolutionary Support Vector Regression Machines
SYNASC '06 Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Maximum margin clustering made practical
Proceedings of the 24th international conference on Machine learning
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Self-Adaptive Heuristics for Evolutionary Computation
Self-Adaptive Heuristics for Evolutionary Computation
Fast evolutionary maximum margin clustering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Evolutionary tuning of multiple SVM parameters
Neurocomputing
A manifold representation as common basis for action production and recognition
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Local meta-models for optimization using evolution strategies
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Evolutionary optimization of sequence kernels for detection of bacterial gene starts
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Bi-population CMA-ES agorithms with surrogate models and line searches
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
Kernel based techniques have shown outstanding success in data mining and machine learning in the recent past. Many optimization problems of kernel based methods suffer from multiple local optima. Evolution strategies have grown to successfulmethods in non-convex optimization. This work shows how both areas can profit from each other. We investigate the application of evolution strategies to Nadaraya-Watson based kernel regression and vice versa. The Nadaraya-Watson estimator is used as meta-model during optimization with the covariance matrix self-adaptation evolution strategy. An experimental analysis evaluates the meta-model assisted optimization process on a set of test functions and investigates model sizes and the balance between objective function evaluations on the real function and on the surrogate. In turn, evolution strategies can be used to optimize the embedded optimization problem of unsupervised kernel regression. The latter is fairly parameter dependent, and minimization of the data space reconstruction error is an optimization problem with numerous local optima. We propose an evolution strategy based unsupervised kernel regression method to solve the embedded learning problem. Furthermore, we tune the novel method by means of the parameter tuning technique sequential parameter optimization.