Master this deck with 16 terms through effective study methods.
Generated from uploaded pdf
An algorithm for supervised learning of binary classifiers.
It integrates multiple signals and generates an output if a threshold is exceeded.
It is a mathematical function modeled on biological neurons.
It determines if the output signal is generated based on the weighted input.
The Perceptron outputs a signal indicating activation.
It allows the algorithm to learn optimal weight coefficients automatically.
Single layer can only learn linearly separable patterns; multilayer can handle non-linear patterns.
It indicates whether the neuron is triggered based on the weighted input.
They determine the contribution of each input to the output.
It outputs probabilities for different classes, summing to 1.
It outputs values between 0 and 1, useful for probability mapping.
It adjusts the decision boundary without dependence on input values.
It ranges between -1 and +1, improving convergence in training.
It eliminates negative outputs, enhancing performance in deep networks.
It takes a linear combination of input and weight vectors to determine output.
The output is -1, indicating the neuron did not trigger.