matlab sigmoid perceptron linear training small, round blue-cell tumor classification example
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Matlab Sigmoid Perceptron Linear Training Small, Round Blue-Cell Tumor Classification Example Matlab Program for NN Analysis Algebraic Training of a Neural Network. You can use various shapes of non-linear neurons in Neural Networks. Perceptron Neural Networks. - PowerPoint PPT PresentationTRANSCRIPT
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• Matlab• Sigmoid• Perceptron• Linear• Training• Small, Round Blue-Cell Tumor Classification
Example• Matlab Program for NN Analysis• Algebraic Training of a Neural Network
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You can use various shapes of non-linear
neurons inNeural Networks
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Perceptron Neural
Networks
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Biases versus Weights in Perceptrons
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Multi-Layer Perceptrons can classify with Boundaries and Clusters
1. Multi-layer perceptrons have more powerful classification capabilities
2. Based on number of layers and elements used we can make a classification of classifiers
3. We can find open and closed regions in classifications.
4. The next slide will show various strengths of classifiers.
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Sigmoid Neural
Networks
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Smooth nonlinearity!
Logsig = logistic sigmoid
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Sigmoid Neural Network
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Single Sigmoid Layer is Sufficient for any continuous function!
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Typical Sigmoid Neural Network Output – create your own mapping
• Sigmoid gives arbitrary accuracy!
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Thresholded Neural Network Output
• Strict YES / NO decision is sometimes useful
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Linear Neural
Networks
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Training Error and Cost for a Single Linear Neuron
Training error
Quadratic error cost
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Linear Neuron GradientTraining errorQuadratic error
costSingle Linear Neuron
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Steepest-Descent Learning for a Single Linear Neuron
New training parameter
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Backpropagation – for a Single Linear Neuron
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Backpropagation for a Single Linear Neuron
Training Sets
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Example: Microarray Training Set
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Steepest-Descent Algorithm for a Single-Step Perceptron
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Training Sigmoid
Networks
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Training Variables for a Single Sigmoid Neuron
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Training a Single Sigmoid Neuron
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Training a Single Sigmoid Neuron
Final formula for weights and biases
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Training a Sigmoid Network
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Training a Sigmoid Network
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Training a Sigmoid NetworkFrom previous slides
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Example of Classification done by a Neural Network
• 62 samples from microarray• 7000 genes• 8 genes in strong feature set
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Small, Round Blue-Cell Tumor
Classification Example
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Small, Round Blue-Cell Tumor Classification Example
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How the training set was created?
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Neural Network Training for the SRBCT problem
• Characteristics of training method for SRBCT
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“Leave-One-Out” – another validation methodology
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“Novel-Set” Validation methodology
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Characteristics of methods applied
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Cardiac Pacemaker
and EKG Signals
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MATLAB for
Neural Networks
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Matlab Program for NN Analysis
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Algebraic Training of a
Neural Network
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conclusions
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