Pattern Classification

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7• There is no automatic method for determining thenonlinearities when no information is provided to theclassifier• In using the multilayer neural Networks or multilayerPerceptrons, the form of the nonlinearity is learned fromthe training data• Multilayer networks can, in principle, provide the optimalsolution to an arbitrary classification problem• Nothing “magical” about multilayer neural networks; theyimplement linear discriminants but in a space where theinputs have been mapped nonlinearlyPattern Classification, Chapter 6
8Feedforward Operation andClassification• A three-layer neural network consists of aninput layer, a hidden layer and an output layerinterconnected by modifiable weightsrepresented by links between layers• Multilayer neural network implements lineardiscriminants, but in a space where the inputshave been mapped nonlinearly• Figure 6.1 shows a simple three-layernetworkPattern 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 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 22 and 23: Backpropagation Algorithm21• Any
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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