What Is The Margin In Support Vector Machines? - The Friendly Statistician
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𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍 SUPPORT VECTOR MACHINES – PART 5.1: INTRO & INTUITION Support Vector Machines (SVMs) don’t just draw any line — they carefully choose the one that maximizes the margin between classes. The "margin" is the distance between the decision boundary and the nearest data points from each class (called support vectors). This makes SVMs robust: they’re less likely to overfit noisy data and more likely to generalize well to unseen examples. You can
𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍 SUPPORT VECTOR MACHINES – PART 5.1: INTRO & INT…
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What Is The Intuition Behind Support Vector Machines? - The Friendly Statistician
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YouTubeThe Friendly Statistician
LMS Solution on Instagram: "Discover how Support Vector Machine (SVM) powered with MATLAB & Simulink can detect and classify electrical faults in both distribution and transmission systems with 96.7% accuracy. Watch the video: https://zurl.co/GQrEm 🔍 In this video, you’ll learn: ✅ Basics of SVM (hyperplanes, kernels, tuning C & gamma) ✅ Data collection strategy for fault simulation ✅ Step-by-step SVM training using MATLAB Classification Learner ✅ Real-time fault detection in Simulink ✅ Applicat
LMS Solution on Instagram: "Discover how Support Vector Machine (SVM) …
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𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙉𝙖𝙩𝙞𝙤𝙣📊 on Instagram: "📍 SUPPORT VECTOR MACHINES – PART 5.2: MATH & EQUATIONS Support Vector Machines (SVMs) work by finding the hyperplane that separates data points of different classes with the widest possible margin. This margin is the distance between the hyperplane and the nearest points from each class (called support vectors). Maximizing this gap isn’t just aesthetic — it actually improves generalization. A wider margin means the classifier is less sensiti
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