Support vector machine
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A Support Vector Machine (SVM) is a supervised machine learning algorithm that finds an optimal decision boundary — called a hyperplane — to classify data into distinct categories by maximizing the margin between classes. In cases where data is not linearly separable, SVMs use mathematical functions called kernels to map data into higher-dimensional spaces where separation becomes possible. In robotics and AI, SVMs are widely applied across perception and recognition tasks, including gesture recognition for teleoperation, EMG-based rehabilitation control, fruit detection for agricultural robots, surface texture classification, place recognition for mobile robots, facial expression analysis, and tool condition monitoring. Their ability to perform well with relatively small, high-dimensional datasets makes them particularly valuable in scenarios where labeled training data is scarce. SVMs matter because they offer strong generalization performance, robustness to overfitting, and mathematical interpretability — qualities that complement deep learning approaches. While neural networks often dominate large-scale tasks, SVMs remain a reliable, computationally efficient choice for structured classification problems in embedded and real-time robotic systems where transparency and reliability are critical.
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