Support vector machines (SVMs) are a supervised machine learning algorithm used for classification and regression tasks.
They are known for their effectiveness in high-dimensional spaces and their ability to handle non-linear relationships between data points.
SVMs are a type of kernel-based learning algorithm that finds the optimal hyperplane that separates two classes of data points.
The optimal hyperplane is the one that maximizes the margin, which is the distance between the hyperplane and the nearest data points from each class.
Goals of SVMs
The main goals of SVMs are to:
- Find the optimal hyperplane that separates two classes of data points.
- Maximize the margin between the hyperplane and the nearest data points from each class.
- Minimize the misclassification error.
Common Tasks in SVM
- Binary classification: Classifying data into two classes, such as spam or not spam, or positive or negative.
- Multi-class classification: Classifying data into more than two classes, such as digits (0-9) or categories (sports, entertainment, business).
- Regression: Predicting a continuous numerical value, such as the price of a house or the temperature tomorrow.
There are many different SVM algorithms, but some of the most common include:
- Linear SVM: This is the simplest type of SVM, and it assumes that the data is linearly separable.
- Nonlinear SVM: This type of SVM can be used for data that is not linearly separable, and it uses a kernel function to transform the data into a higher-dimensional space where it can be separated linearly.
- Support vector regression (SVR): This is an extension of SVMs to regression tasks.
Applications of SVMs
- Image recognition: SVMs are used to recognize objects in images, such as faces or cars.
- Natural language processing (NLP): SVMs are used for tasks such as sentiment analysis, spam filtering, and document classification.
- Bioinformatics: SVMs are used to analyze biological data, such as gene expression data and protein sequences.
- Finance: SVMs are used to predict stock prices and assess creditworthiness.
Limitations of SVMs
- Sensitivity to outliers: SVMs can be sensitive to outliers, which can make them difficult to train on noisy data.
- High computational cost: Training SVMs can be computationally expensive, especially for large datasets.
- Difficulty in selecting the right kernel function: The choice of kernel function can have a significant impact on the performance of an SVM.
- “Support Vector Machines: Theory and Algorithms” by Nello Cristianini and John Shawe-Taylor
- “Practical Support Vector Machines” by Chang and Chih-Chung Hsu