Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An equivalence between sparse approximation and support vector machines
Neural Computation
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Priors Stabilizers and Basis Functions: From Regularization to Radial, Tensor and Additive Splines
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Efficient computations via scalable sparse kernel partial least squares and boosted latent features
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Multi-Output Regularized Feature Projection
IEEE Transactions on Knowledge and Data Engineering
Kernel Methods for Measuring Independence
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Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
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Expert Systems with Applications: An International Journal
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International Journal of Bioinformatics Research and Applications
Expert Systems with Applications: An International Journal
Learning subspace kernels for classification
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Multi-Scale Kernel Latent Variable Models for Nonlinear Time Series Pattern Matching
Neural Information Processing
Data description and noise filtering based detection with its application and performance comparison
Expert Systems with Applications: An International Journal
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Nonlinear process modeling and optimization based on multiway kernel partial least squares model
Proceedings of the 40th Conference on Winter Simulation
Semi-supervised Discriminant Analysis Based on Dependence Estimation
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
A subspace kernel for nonlinear feature extraction
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Identification of non linear MISO process using RKHS and Volterra models
WSEAS TRANSACTIONS on SYSTEMS
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User Modeling and User-Adapted Interaction
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Subset based least squares subspace regression in RKHS
Neurocomputing
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Computational Statistics & Data Analysis
Predictive K-PLSR myocardial contractility modeling with phase contrast MR velocity mapping
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Expert Systems with Applications: An International Journal
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Artificial Intelligence
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Expert Systems with Applications: An International Journal
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Applied Soft Computing
Predictive modeling of cardiac fiber orientation using the knutsson mapping
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Clinical validation of machine learning for automatic analysis of multichannel magnetocardiography
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
Nonlinear kernel MSE methods for cancer classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Dimension reduction vs. variable selection
PARA'04 Proceedings of the 7th international conference on Applied Parallel Computing: state of the Art in Scientific Computing
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AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
A novel nonlinear neural network ensemble model using K-PLSR for rainfall forecasting
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Expert Systems with Applications: An International Journal
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ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Learning kernel subspace classifier
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Churn management optimization with controllable marketing variables and associated management costs
Expert Systems with Applications: An International Journal
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Pattern Recognition Letters
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Environmental Modelling & Software
Computational Statistics & Data Analysis
Bayesian sparse partial least squares
Neural Computation
Supervised Distance Preserving Projections
Neural Processing Letters
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A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model. Similar to principal components regression (PCR), PLS is a method based on the projection of input (explanatory) variables to the latent variables (components). However, in contrast to PCR, PLS creates the components by modeling the relationship between input and output variables while maintaining most of the information in the input variables. PLS is useful in situations where the number of explanatory variables exceeds the number of observations and/or a high level of multicollinearity among those variables is assumed. Motivated by this fact we will provide a kernel PLS algorithm for construction of nonlinear regression models in possibly high-dimensional feature spaces.We give the theoretical description of the kernel PLS algorithm and we experimentally compare the algorithm with the existing kernel PCR and kernel ridge regression techniques. We will demonstrate that on the data sets employed kernel PLS achieves the same results as kernel PCR but uses significantly fewer, qualitatively different components.