Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence Review - Special issue on lazy learning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
On global, local, mixed and neighborhood kernels for support vector machines
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
Pattern Selection for Support Vector Classifiers
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Inference for the Generalization Error
Machine Learning
Machine Learning
Statistics and Computing
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
A local boosting algorithm for solving classification problems
Computational Statistics & Data Analysis
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Local Modelling in Classification
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Principles and Theory for Data Mining and Machine Learning
Principles and Theory for Data Mining and Machine Learning
Efficient Algorithm for Localized Support Vector Machine
IEEE Transactions on Knowledge and Data Engineering
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Modern Applied Statistics with S
Modern Applied Statistics with S
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Mixtures of kernels for SVM modeling
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Local linear perceptrons for classification
IEEE Transactions on Neural Networks
Sample selection via clustering to construct support vector-like classifiers
IEEE Transactions on Neural Networks
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In recent years in the fields of statistics and machine learning an increasing amount of so called local classification methods has been developed. Local approaches to classification are not new, but have lately become popular. Well-known examples are the $$k$$ nearest neighbors method and classification trees. However, in most publications on this topic the term "local" is used without further explanation of its particular meaning. Only little is known about the properties of local methods and the types of classification problems for which they may be beneficial. We explain the basic principles and introduce the most important variants of local methods. To our knowledge there are very few extensive studies in the literature that compare several types of local methods and global methods across many data sets. In order to assess their performance we conduct a benchmark study on real-world and synthetic tasks. We cluster data sets and considered learning algorithms with regard to the obtained performance structures and try to relate our theoretical considerations and intuitions to these results. We also address some general issues of benchmark studies and cover some pitfalls, extensions and improvements.