Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Fundamentals of speech recognition
Fundamentals of speech recognition
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Efficient computations for large least square support vector machine classifiers
Pattern Recognition Letters
SMO algorithm for least-squares SVM formulations
Neural Computation
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Learning with non-positive kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Kernels, regularization and differential equations
Pattern Recognition
A look-ahead Levinson algorithm for general Toeplitz systems
IEEE Transactions on Signal Processing
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Machine Learning based on the Regularized Least Squares (RLS) model requires one to solve a system of linear equations. Direct-solution methods exhibit predictable complexity and storage, but often prove impractical for large-scale problems; iterative methods attain approximate solutions at lower complexities, but heavily depend on learning parameters. The paper shows that applying the properties of Toeplitz matrixes to RLS yields two benefits: first, both the computational cost and the memory space required to train an RLS-based machine reduce dramatically; secondly, timing and storage requirements are defined analytically. The paper proves this result formally for the one-dimensional case, and gives an analytical criterion for an effective approximation in multidimensional domains. The approach validity is demonstrated in several real-world problems involving huge data sets with highly dimensional data.