Core Insights - The support vector machine (SVM) market in China is projected to reach approximately 428 million yuan in 2024, reflecting a year-on-year growth of 10.03% as domestic enterprises accelerate their digital transformation [1][8] - Despite its widespread applications, SVM faces challenges such as limitations in efficiency and scalability when handling large datasets, and competition from emerging technologies like deep learning [1][8] - SVM retains unique advantages in processing small sample and high-dimensional data, particularly in fields requiring high model interpretability [1][8] Industry Overview - SVM is a supervised learning algorithm primarily used for classification and regression analysis, focusing on finding an optimal hyperplane in feature space to maximize the margin between different classes [2] - The SVM industry chain includes upstream components like high-performance computing chips and sensors, midstream algorithm development and service providers, and downstream applications in finance, healthcare, industry, education, and retail [3][4] Market Size - The SVM market in China is on an upward trajectory, with a projected market size of approximately 428 million yuan in 2024, marking a 10.03% increase from the previous year [8] - The growth is driven by the increasing demand for SVM in various sectors, despite the challenges posed by larger data scales and the rise of deep learning technologies [8] Key Companies - Major players in the SVM industry include internet giants like Baidu, Alibaba, and Tencent, which leverage their financial resources, advanced technologies, and rich data resources to dominate the market [8] - Companies like Zhuhai Yichuang and Nine Chapters Cloud Technology are also making significant strides in the SVM field, providing machine learning platforms and automated modeling tools [8] Industry Development Trends - Future trends indicate a deep integration of SVM with deep learning technologies, enhancing model performance and generalization capabilities [12] - The development of more efficient optimization algorithms and distributed computing frameworks is expected to address SVM's computational efficiency issues, particularly for large datasets [13] - The emergence of quantum computing presents new opportunities for SVM, with quantum support vector machines (QSVM) showing promise in handling high-dimensional data and complex problems [15]