Enhanced Multi-class Pattern Recognition Support with Maxwins Algorithm

This release introduces significant advancements in our pattern recognition framework, with core implementation focusing on multi-class classification support. The system now integrates three robust algorithms: 1) Maxwins voting mechanism with parallel processing implementation for scalable class prediction 2) Pairwise decomposition (one-vs-one) strategy using optimized binary SVM classifiers 3) Directed Acyclic Graph SVM (DAG-SVM) with efficient path optimization to reduce computational complexity A key innovation is the integration of the xi-alpha bound criterion for model selection, which implements a theoretical upper bound on leave-one-out cross-validation error. This enables automated model evaluation through our select_model() function, incorporating: - Error bound calculation using dual variables from SVM optimization - Hyperparameter grid search with early stopping based on bound thresholds - Visualization tools for comparing model performance metrics These enhancements are implemented through modular Python classes (MulticlassSVM, ModelSelector) featuring: - Batch processing capabilities for large datasets - GPU acceleration support via CuPy integration - Compatibility with scikit-learn API standards The framework now delivers superior performance in complex recognition tasks including image classification (CNNs integration ready), speech recognition feature mapping, and multi-label text categorization, providing researchers with production-ready code architecture and comprehensive documentation.