Neural Networks for Applied Sciences and Engineering--Chapter 2
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Chapter 2 Fundamentals of Neural Networks and Models for Linear Data Analysis
The full article:http://note.youdao.com/noteshare?id=909ec7e9da92bccc62de17a2feb04a40
2.5 Neuron Models and Learning Strategies
2.5.1 Threshold Neuron as a Simple Classifier
2.5.2 Learning Models for Neurons and Neural Assemblies
2.5.2.1 Hebbian Learning
Maybe Hebbian Learning is better to do unsupervised or competitive learning!?
2.5.3 Perceptron with Supervised Learning as a Classifier
2.5.3.1 Perceptron Learning Algorithm
It could demonstrate the new omiga can make the new u approach to the true value than the old.For example:
2.5.4 Linear Neuron for Linear Classification and Prediction
2.5.4.1 Learning with the Delta Rule
example-by-example learning versus batch learning
2.5.4.9 Multiple Linear Neuron Models
because Multiple Linear Neuron Models adjusts weights by cost function which is related to all yi, it studies the simultaneous effect on several dependent variables.
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