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Data Applied: Forecast


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Data Applied: Forecast

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Data Applied: Forecast

  1. 1. 5<br /> Forecast<br />
  2. 2. Perceptron<br />Perceptron can be used for linear classification<br />Linear classification using the perceptron<br />If instances belonging to different classes can be divided in the instance space by using hyper planes, then they are called linearly separable<br />If instances are linearly separable then we can use perceptron learning rule for classification <br />
  3. 3. Multilayer Perceptron<br />Multilayer perceptron:<br />We can create a network of perceptron to approximate arbitrary target concepts <br />Multilayer perceptron is an example of an artificial neural network<br />Consists of: input layer, hidden layer(s), and output layer <br /> Structure of MLP is usually found by experimentation<br />Parameters can be found using back propagation or montecarlo simulations <br />
  4. 4. Example of multilayer perceptron<br />
  5. 5. Error metric<br />The parameters to be selected such that the minimum error is produced<br />Error metric used:<br />f(x) = 1/(1+exp(-x))<br />Error = ½(y-f(x))^2<br />
  6. 6. Using montecarlo to get the parameters<br />Distribute some random samples in the weight vector space<br />Choose the ones which minimizes errors<br />Repeat the process till convergence<br />Finally points at the convergence gives us the value of the parameters<br />
  7. 7. Forecasts using Data Applied’s web interface<br />
  8. 8. Step1: Selection of data<br />
  9. 9. Step2: Selecting Forecasts<br />
  10. 10. Step3: Result<br />
  11. 11. Visit more self help tutorials<br /><ul><li>Pick a tutorial of your choice and browse through it at your own pace.
  12. 12. The tutorials section is free, self-guiding and will not involve any additional support.
  13. 13. Visit us at</li>