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SerializationDemo
- These instances, whenmapped to an N-dimensional space, represent a core set that can be used to construct an approximation to theminimumenclosing ball. Solving the SVMlearning problem on these core sets can produce a good approximation solution i
SupportVectorMachineasanEfficientFrameworkforStock
- Abstract Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract i
3836
- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, The received signal is given eye and BER simulation systems, Principal component analysis of multivariate data analysis projection.
jou_gx83
- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Can realize the two-dimensional data clustering, wolf calculated Lyapunov exponent.
pkume
- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, ECG data and includes source code written in MATLAB, It can be directly calculated multi-fractal spectrum.