The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Fast learning in networks of locally-tuned processing units
Neural Computation
Kernel matching pursuit for large datasets
Pattern Recognition
A new dictionary learning method for kernel matching pursuit
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Hidden space support vector machines
IEEE Transactions on Neural Networks
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Inspired by kernel matching pursuit (KMP) and support vector machines (SVMs), we propose a novel classification algorithm: kernel matching reduction algorithm (KMRA). This method selects all training examples to construct a kernel-based functions dictionary. Then redundant functions are removed iteratively from the dictionary, according to their weights magnitudes, which are determined by linear support vector machines (SVMs). During the reduction process, the parameters of the functions in the dictionary can be adjusted dynamically. Similarities and differences between KMRA and several other machine learning algorithms are also addressed. Experimental results show KMRA can have sparser solutions than SVMs, and can still obtain comparable classification accuracies to SVMs.