A resource-allocating network for function interpolation
Neural Computation
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Machine Learning
Reduced Rank Kernel Ridge Regression
Neural Processing Letters
Sparse on-line Gaussian processes
Neural Computation
A parallel mixture of SVMs for very large scale problems
Neural Computation
SMO algorithm for least-squares SVM formulations
Neural Computation
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Some greedy learning algorithms for sparse regression and classification with mercer kernels
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Predictive low-rank decomposition for kernel methods
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
Boosting Kernel Models for Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Kernel least-squares models using updates of the pseudoinverse
Neural Computation
Training a Support Vector Machine in the Primal
Neural Computation
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
The Journal of Machine Learning Research
Large-scale RLSC learning without agony
Proceedings of the 24th international conference on Machine learning
Evidence Contrary to the Statistical View of Boosting
The Journal of Machine Learning Research
Kernel conjugate gradient for fast kernel machines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Sequential learning with LS-SVM for large-scale data sets
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
Continuously Differentiable Sample-Spacing Entropy Estimation
IEEE Transactions on Neural Networks
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Semisupervised kernel matrix learning by kernel propagation
IEEE Transactions on Neural Networks
Condensed vector machines: learning fast machine for large data
IEEE Transactions on Neural Networks
A fast method of feature extraction for kernel MSE
Neurocomputing
Pruning least objective contribution in KMSE
Neurocomputing
Regularized vector field learning with sparse approximation for mismatch removal
Pattern Recognition
Training sparse SVM on the core sets of fitting-planes
Neurocomputing
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Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subset of original data points also known as basis vectors, which are usually chosen one by one with a forward selection procedure based on some selection criteria. The computational complexity of several resultant algorithms scales as O(NM2)in time and O(NM) in memory, where is N the number of training points and M is the number of basis vectors as well as the steps of forward selection. For some large scale data sets, to obtain a better solution, we are sometimes required to include more basis vectors, which means that M is not trivial in this situation. However, the limited computational resource (e.g., memory) prevents us from including too many vectors. To handle this dilemma, we propose to add an ensemble of basis vectors instead of only one at each forward step. The proposed method, closely related to gradient boosting, could decrease the required number M of forward steps significantly and thus a large fraction of computational cost is saved. Numerical experiments on three large scale regression tasks and a classification problem demonstrate the effectiveness of the proposed approach.