# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### (2) Support Vector Machine Model  
The Support Vector Machine (SVM) [82] is a powerful algorithm widely used in machine learning for both classification and regression tasks. As a supervised learning method, its primary objective is to identify an optimal hyperplane in the feature space that effectively separates different classes of data points while maximizing the margin between them. The fundamental principle of SVM is to determine the hyperplane that best divides the dataset, which, in a d-dimensional feature space, corresponds to a (d-1)-dimensional subspace. Figure 6b shows the core concept of SVM. In the feature space, SVM looks for an optimal hyperplane (Maximum Margin Hyperplane) to separate samples of different categories. Sample points on the boundary are called support vectors. The distance between the positive hyperplane (Positive Hyperplane) and the negative hyperplane (Negative Hyperplane) is maximized to improve the generalization ability of the model. To achieve this, SVM leverages support vectors--data points that are closest to the hyperplane--which play a crucial role in defining its position and orientation [83]. One \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Comparison Criteria** & **Supervised Learning** & **Unsupervised Learning** & **Semi-Supervised Learning** & **Reinforcement Learning** \\ \hline
**Definition** & Utilizes labeled data to train models for predicting outputs of new input data. & Employs unlabeled data to discover inherent structures or patterns within the data. & Combines a small amount of labeled data with a large volume of unlabeled data to enhance learning performance. & Involves an agent interacting with the environment to learn strategies that maximize cumulative rewards. \\ \hline
**Data Requirements** & Requires a substantial amount of high-quality labeled data. & Needs a large quantity of unlabeled data. & Uses a combination of a small set of labeled data and a large set of unlabeled data. & Gathers data through small set of labeled data and a large set of unlabeled data. & Gathers data through interactions with the environment, eliminating the need for pre-labeled data. \\ \hline
**Primary Tasks** & Classification, regression. & Clustering, dimensionality reduction, association rule learning. & Classification, regression, semi-supervised clustering. & Sequential decision-making, policy optimization. \\ \hline
**Common Algorithms** & Linear Regression, Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Neural Networks. & K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), Autoencoders. & Self-training, Autoencoders, Graph-based Semi-Supervised Learning. & Q-Learning, Deep Q Network (DQN), Policy Gradient Methods, Actor-Critic Methods. \\ \hline
**Advantages** & High predictive accuracy with interpretable results. & Capable of uncovering hidden patterns in data without the need for labeled data. & Enhances model performance by leveraging unlabeled data, reducing labeling costs. & Effective in learning optimal strategies within complex and uncertain environments. \\ \hline
**Disadvantages** & Dependence on large amounts of labeled data, leading to high labeling costs. & Results may lack clear interpretability and challenging to evaluate. & Increased model complexity and potential instability during training. & Learning processes may require extensive interactions, resulting in long training times and high computational resource demands. \\ \hline
**Application Areas** & Image recognition, speech recognition, spam detection, stock price prediction. & Customer segmentation, anomaly detection, gene data analysis, market basket analysis. & Medical image analysis, text classification, web content categorization. & Game AI, robotic control, autonomous driving, resource management. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative analysis of the four major machine learning paradigms.