Final answer:
Perceptron learning, delta learning, and LMS learning are supervised learning techniques in machine learning, used for training artificial neural networks by adjusting weights based on input data to perform specific tasks.
Step-by-step explanation:
The perceptron learning, delta learning, and LMS learning methods all fall under the category of supervised learning techniques in artificial intelligence, specifically within the realm of machine learning. These algorithms are used to train artificial neurons or nodes in a network to perform tasks like classification and regression by adjusting weights based on input data and desired outputs.
Each method approaches the learning process differently. Perceptron learning adjusts the weights after each training example is processed, which makes it suitable for simple problems where data is linearly separable. Delta learning, also known as the delta rule or gradient descent, modifies the weights based on the derivative of a cost function concerning the weights, aiming to minimize the difference between actual and predicted outputs. Lastly, LMS (Least Mean Squares) learning, employs a similar approach to delta learning but updates the weights to minimize the mean square error between the predicted output and actual output.
These methods embody the fundamental principles of cognitive learning within the field of machine learning, constantly adjusting and improving through experience and exposure to new data, much like how humans learn from practice and repetition.