Support Vector Machine (SVM) Theoretical Foundation: Statistical Learning Theory and SVM Principles

High-quality research paper with accompanying source code. This study first reviews the application research status of load forecasting, summarizes the characteristics and influencing factors of load forecasting, categorizes common methods for short-term load forecasting, and analyzes the advantages and disadvantages of various methods. It then introduces the statistical learning theory as the theoretical foundation of Support Vector Machines (SVM) and explains SVM principles, deriving the SVM regression model. The paper employs a Least Squares Support Vector Machine (LSSVM) model, utilizing historical load data and meteorological data from Taizhou, Zhejiang Province to analyze various factors affecting predictions and summarize load variation patterns. The implementation includes preprocessing steps such as correcting "abnormal data" in historical load records and normalizing relevant factors for load forecasting. The study specifically addresses the significant impact of two key parameters in LSSVM models, which are currently determined empirically. The methodology incorporates parameter optimization using Particle Swarm Optimization (PSO) algorithm, where test set error serves as the criterion for parameter selection, demonstrating improved prediction accuracy through systematic parameter tuning.

SVM-Based Image Segmentation - True Color Image Segmentation

Image segmentation represents a critical research domain in image processing, serving as the foundational step for image analysis. Its primary objective is to extract target objects of interest from images. While numerous scholars worldwide have proposed various segmentation methodologies, these approaches generally lack universality across diverse image types, typically demonstrating effectiveness only for specific image categories. Support Vector Machine (SVM), a classification technique rooted in statistical learning theory, has gained extensive application across multiple domains including pattern recognition, data classification, and image segmentation. Renowned for its strong generalization capabilities, SVM has emerged as a prevalent trend in image segmentation implementations, consistently delivering superior segmentation outcomes. This article explores SVM-based image segmentation methodologies with particular focus on implementation approaches and algorithmic enhancements.