Neural Computation
Local algorithms for pattern recognition and dependencies estimation
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Support vector machines are universally consistent
Journal of Complexity
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Combined SVM-Based Feature Selection and Classification
Machine Learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Projectable Classifiers of Arbitrary Complexity
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Data-Dependent Kernel Machines for Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Scalable Noise Reduction Technique for Large Case-Based Systems
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Fast Local Support Vector Machines for Large Datasets
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Consistency of support vector machines and other regularized kernel classifiers
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Benchmarking local classification methods
Computational Statistics
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Motivated by the crucial role that locality plays in various learning approaches, we present, in the framework of kernel machines for classification, a novel family of operators on kernels able to integrate local information into any kernel obtaining quasi-local kernels. The quasi-local kernels maintain the possibly global properties of the input kernel and they increase the kernel value as the points get closer in the feature space of the input kernel, mixing the effect of the input kernel with a kernel which is local in the feature space of the input one. If applied on a local kernel the operators introduce an additional level of locality equivalent to use a local kernel with non-stationary kernel width. The operators accept two parameters that regulate the width of the exponential influence of points in the locality-dependent component and the balancing between the feature-space local component and the input kernel. We address the choice of these parameters with a data-dependent strategy. Experiments carried out with SVM applying the operators on traditional kernel functions on a total of 43 datasets with different characteristics and application domains, achieve very good results supported by statistical significance.