The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Towards scalable support vector machines using squashing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Lagrangian support vector machines
The Journal of Machine Learning Research
Training multilayer perceptron classifiers based on a modified support vector method
IEEE Transactions on Neural Networks
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
An efficient star acquisition method based on SVM with mixtures of kernels
Pattern Recognition Letters
Online Least Squares Support Vector Machines Based on Wavelet and Its Applications
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Research of CT / MRI Tumor Image Registration Based on Least Square Support Vector Machines
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Nonparallel plane proximal classifier
Signal Processing
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
Help-training semi-supervised LS-SVM
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Efficient approximate Regularized Least Squares by Toeplitz matrix
Pattern Recognition Letters
Help-Training for semi-supervised support vector machines
Pattern Recognition
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Improved conjugate gradient implementation for least squares support vector machines
Pattern Recognition Letters
An adaptive support vector machine learning algorithm for large classification problem
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Research of neural network classifier based on FCM and PSO for breast cancer classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Efficient sparse least squares support vector machines for pattern classification
Computers & Mathematics with Applications
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
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We observed that the linear system in the training of the least square support vector machine (LSSVM) proposed by Suykens and Vandewalle (Neural process. Lett. 9 (1999a) 293-300; IEEE Trans. Neural Networks 10(4) (1999b) 907-912) can be placed in a more symmetric form so that for a data set with N data points and m features, the linear system can be solved by inverting an m × m instead of an N × N matrix and storing and working with matrices of size at most m × N. This allows us to apply LSSVM to very large data set with small number of features. Our computations show that a data set with a million data points and 10 features can be trained in only 45 s. We also compared the effectiveness and efficiency of our method to standard LSSVM and standard SVM. An example using a quadratic kernel is also given.