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The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).
Learning inside a single-layer ADALINE Photo of an ADALINE machine, with hand-adjustable weights implemented by rheostats Schematic of a single ADALINE unit [1]. ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented it.
Depending on the complexity of the model being simulated, the learning rule of the network can be as simple as an XOR gate or mean squared error, or as complex as the result of a system of differential equations. The learning rule is one of the factors which decides how fast or how accurately the neural network can be developed.
The bias–variance decomposition forms the conceptual basis for regression regularization methods such as LASSO and ridge regression.Regularization methods introduce bias into the regression solution that can reduce variance considerably relative to the ordinary least squares (OLS) solution.
Download as PDF; Printable version; ... based on their research in single-layer neural networks ... is the mean square error, and it is minimized by the LMS. This is ...
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Bernard Widrow (born December 24, 1929) is a U.S. professor of electrical engineering at Stanford University. [1] He is the co-inventor of the Widrow–Hoff least mean squares filter (LMS) adaptive algorithm with his then doctoral student Ted Hoff. [2]
Folding activation functions are extensively used in the pooling layers in convolutional neural networks, and in output layers of multiclass classification networks. These activations perform aggregation over the inputs, such as taking the mean, minimum or maximum. In multiclass classification the softmax activation is often used.