Neural Computation
Local algorithms for pattern recognition and dependencies estimation
Neural Computation
Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
On global, local, mixed and neighborhood kernels for support vector machines
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
ACM Computing Surveys (CSUR)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A parallel mixture of SVMs for very large scale problems
Neural Computation
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
Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing
Data Mining and Knowledge Discovery
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Circular backpropagation networks for classification
IEEE Transactions on Neural Networks
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
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
ICADL'10 Proceedings of the role of digital libraries in a time of global change, and 12th international conference on Asia-Pacific digital libraries
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Hi-index | 0.00 |
Local SVM is a classification approach that combines instance-based learning and statistical machine learning. It builds an SVM on the feature space neighborhood of the query point in the training set and uses it to predict its class. There is both empirical and theoretical evidence that Local SVM can improve over SVM and k NN in terms of classification accuracy, but the computational cost of the method permits the application only on small datasets. Here we propose FastLSVM, a classifier based on Local SVM that decreases the number of SVMs that must be built in order to be suitable for large datasets. FastLSVM precomputes a set of local SVMs in the training set and assigns to each model all the points lying in the central neighborhood of the k points on which it is trained. The prediction is performed applying to the query point the model corresponding to its nearest neighbor in the training set. The empirical evaluation we provide points out that FastLSVM is a good approximation of Local SVM and its computational performances on big datasets (a large artificial problem with 100000 samples and a very large real problem with more than 500000 samples) dramatically ameliorate performances of SVM and its fast existing approximations improving also the generalization accuracies.