Support Vector Machine
A Support Vector Machine (SVM) is a classification algorithm that finds the optimal hyperplane separating different classes with maximum margin. SVMs are effective for high-dimensional data and small datasets.
Understanding Support Vector Machine
Support Vector Machines (SVMs) are powerful supervised learning models that find the optimal hyperplane separating different classes with the maximum margin between the closest data points of each class. The kernel trick allows SVMs to handle non-linearly separable data by implicitly mapping inputs into higher-dimensional spaces where linear separation becomes possible. SVMs excel in high-dimensional settings with limited training data and have been successfully applied to text classification, image recognition, bioinformatics, and handwriting detection. Before the deep learning revolution, SVMs were among the top-performing algorithms for many classification tasks. They remain relevant for small-to-medium datasets and offer strong theoretical guarantees rooted in statistical learning theory, including bounds on generalization error based on the bias-variance tradeoff.
Category
Machine Learning
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Machine Learning Terms
Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.