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The least mean square (LMS) algorithm is a type of filter used in machine learning that uses stochastic gradient descent in sophisticated ways – professionals describe it as an adaptive filter that helps to deal with signal processing in various ways.
The least mean square algorithm uses a technique called “method of steepest descent” and continuously estimates results by updating filter weights. Through the principle of algorithm convergence, the least mean square algorithm provides particular learning curves useful in machine learning theory and implementation. Many of these ideas are part of dedicated work on refining machine learning models, matching inputs to outputs, making training and test processes more effective, and generally pursuing “convergence” where the iterative learning process resolves into a coherent final result instead of getting off track.