Breast Cancer Dataset for Machine Learning Research
The breast cancer dataset serves as a critical benchmark for studying support vector machines, sample selection methods, and kernel methods in machine learning applications.
Kernel Methods for Pattern Analysis Source Code
Source code from the book "Kernel Methods for Pattern Analysis" featuring comprehensive implementation examples, algorithm explanations, and supplementary presentation materials
Kernel Methods and SVM: Essential Techniques in Pattern Recognition
Kernel Methods and Support Vector Machines (SVM) represent fundamental approaches in pattern recognition, each forming a distinct discipline with robust theoretical foundations and practical implementations.
Kernel-FCM Clustering Algorithm: Implementation and Applications
An enhanced fuzzy clustering method combining kernel techniques with Fuzzy C-Means for improved pattern recognition in non-linear data structures.
MATLAB Implementation of Gaussian Kernel Function with Code Descriptions
MATLAB program implementation of the Gaussian kernel function, a commonly used kernel in machine learning, with enhanced technical explanations and implementation details.
Nonlinear Classifiers in Pattern Recognition
Nonlinear Classifiers in Pattern Recognition with Implementation Approaches
Computing Regularization Paths for Multiple Kernel Learning
An algorithmic approach for systematically exploring kernel combinations across varying regularization intensities, with implementation insights for path tracking and optimization.
MATLAB Implementation and Documentation of Kernel Principal Component Analysis (KPCA) Algorithm
Comprehensive MATLAB Implementation and Technical Explanation of Kernel PCA Algorithm with Code-Related Enhancements
MATLAB Implementation of Kernel Fisher Discriminant Analysis
MATLAB Code Implementation of Kernel Fisher Discriminant Analysis for Nonlinear Classification
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis - Techniques and Implementation