Explore MATLAB source code curated for "计算速度" with clean implementations, documentation, and examples.
Kernel Extreme Learning Machine (KELM) enhances the standard Extreme Learning Machine algorithm with superior regression prediction capabilities, improved generalization performance, and faster computational speed while achieving comparable or better prediction accuracy. The algorithm leverages kernel functions to implicitly map input data to high-dimensional feature spaces, enabling efficient nonlinear modeling without explicit feature transformation.
Image denoising with 1D wavelet transform offers lower computational complexity, improved efficiency and faster processing speed through optimized algorithm implementation.
The Harris algorithm is a corner detection method that identifies invariant feature points in images, significantly reducing computational load and accelerating processing speed. However, this approach leads to substantial information loss. The RANSAC (Random Sample Consensus) algorithm calculates mathematical models from point sets, effectively eliminating mismatched Harris corners to produce more authentic and accurate matching results.
A faster computational approach for Support Vector Machines that enhances processing speed, featuring the SVM-demo algorithm implementation.
My improved MATLAB implementation of Extreme Learning Machine (ELM) neural network algorithm demonstrates significantly faster computational speed when using more than 3 neurons. The key enhancement utilizes function-based column matrix generation techniques. ELM is fundamentally a rapid neural network algorithm, and through comparative testing, I've verified it outperforms many popular algorithms (BP, SVM) in both speed and effectiveness. This implementation runs in MATLAB environment and supports testing with all benchmark datasets.
This implementation calculates HU's 7 moment invariants and RADON's 2nd/3rd order moment invariants. The website contains C++ code for RADON transform, while a Northwestern Polytechnical University professor has developed novel invariant moments using RADON transform that reduce image dimensionality with accelerated computation speed. Algorithm details are referenced in academic literature.
Classical iterative histogram separation clustering algorithm for multi-class object segmentation, leveraging image histogram analysis for high computational efficiency suitable for large-scale data processing
The fast deconvolution algorithm achieves rapid computation, with manual adjustment of FFT points in the M-file allowing precision control. This implementation provides excellent performance with optimized signal processing capabilities.
Trinomial tree method for American option pricing delivers superior accuracy and computational efficiency with detailed algorithm documentation included