Pattern Classification

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Backpropagation Algorithm21• Any function from input to output can beimplemented as a three-layer neural network• These results are of greater theoretical interestthan practical, since the construction of such anetwork requires the nonlinear functions and theweight values which are unknown!Pattern Classification, Chapter 6
Network Learning22• Our goal is to learn the interconnection weights based onthe training patterns and the desired outputs• In a three-layer network, it is a straightforward matter tounderstand how the output, and thus the error, dependson the hidden-to-output layer weights• The power of backpropagation is that it enables us tocompute an effective error for each hidden unit, and thusderive a learning rule for the input-to-hidden weights. Thisis known as:The credit assignment problemPattern Classification, Chapter 6
Page 1: PatternClassificationAll materials
Page 4 and 5: Natural Neural Net Models• Human
Page 6 and 7: Linear Discriminant Functions• A
Page 8 and 9: 7• There is no automatic method f
Page 10 and 11: 9Pattern Classification, Chapter 6
Page 12 and 13: 11• A single “bias unit” is c
Page 14 and 15: 13• The output units are referred
Page 16 and 17: • General Feedforward Operation -
Page 18 and 19: 17• Eq (8) can be expressed in ne
Page 20 and 21: 19Pattern Classification, Chapter 6
Page 24 and 25: • Network has two modes of operat
Page 26 and 27: Network Learning• Start with an u
Page 28 and 29: 27Since net k = w kt .y, therefore:
Page 30 and 31: Sensitivity at Hidden Node
Page 32 and 33: • Training protocols:• Stochast
Page 34 and 35: • Learning Curves33• Before tra
Page 36 and 37: Representation at the Hidden Layer
Page 38 and 39: Practical Techniques for ImprovingB
Page 40 and 41: Practical Considerations• Scaling
Page 42 and 43: Total no. of WeightsError per patte
Page 44 and 45: Practical Considerations• Momentu

Pattern Classification

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