The Effect of the Input Density Distribution on Kernel-based Classifiers

Learning curves for Gaussian process regression: approximations and bounds

Neural Computation

Orthogonal series density estimation and the kernel eigenvalue problem

Neural Computation

On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum

ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory

Approximation bounds for some sparse kernel regression algorithms

Neural Computation

Kernel-based nonlinear blind source separation

Neural Computation

RBF neural networks for classification using new Kernel functions

Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications

Kernel independent component analysis

The Journal of Machine Learning Research

The Entire Regularization Path for the Support Vector Machine

The Journal of Machine Learning Research

Predictive low-rank decomposition for kernel methods

ICML '05 Proceedings of the 22nd international conference on Machine learning

Learning Eigenfunctions Links Spectral Embedding and Kernel PCA

Neural Computation

Block-quantized kernel matrix for fast spectral embedding

ICML '06 Proceedings of the 23rd international conference on Machine learning

On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning

The Journal of Machine Learning Research

Statistical properties of kernel principal component analysis

Machine Learning

Kernel logistic PLS: A tool for supervised nonlinear dimensionality reduction and binary classification

Computational Statistics & Data Analysis

Data spectroscopy: learning mixture models using eigenspaces of convolution operators

Proceedings of the 25th international conference on Machine learning

Improved Nyström low-rank approximation and error analysis

Proceedings of the 25th international conference on Machine learning

Density-weighted nyström method for computing large kernel eigensystems

Neural Computation

Prototype vector machine for large scale semi-supervised learning

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning

Granular Computing and Rough Sets to Generate Fuzzy Rules

ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition

A novel kernel-based maximum a posteriori classification method

Neural Networks

Efficient band approximation of Gram matrices for large scale kernel methods on GPUs

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

Accurate Probabilistic Error Bound for Eigenvalues of Kernel Matrix

ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning

On speeding up computation in information theoretic learning

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks

Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks

IEEE Transactions on Information Forensics and Security

Clustered Nyström method for large scale manifold learning and dimension reduction

IEEE Transactions on Neural Networks

Bayesian hybrid generative discriminative learning based on finite Liouville mixture models

Pattern Recognition

Approximating a gram matrix for improved kernel-based learning

COLT'05 Proceedings of the 18th annual conference on Learning Theory

Multi-kernel multi-label learning with max-margin concept network

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two

Distributed approximate spectral clustering for large-scale datasets

Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing

Learning low-rank Mercer kernels with fast-decaying spectrum

Neurocomputing

Active subspace: Toward scalable low-rank learning

Neural Computation

Sparse semi-supervised learning on low-rank kernel

Neurocomputing