In fact, no learner is immune to the curse of dimensionality. It’s the second worst problem in machine learning, after overfitting. The term curse of dimensionality was coined by Richard Bellman, a control theorist, in the fifties. He observed that control algorithms that worked fine in three dimensions became hopelessly inefficient in higher-dimensional spaces, such as when you want to control every joint in a robot arm or every knob in a chemical plant. But in machine learning the problem is more than just computational cost—it’s that learning itself becomes harder and harder as the
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