Like this presentation? Why not share!

# Data Applied: Forecast

## by dataapplied content, Datamining at DMT on Mar 06, 2010

• 404 views

Data Applied: Forecast

Data Applied: Forecast

### Views

Total Views
404
Views on SlideShare
386
Embed Views
18

Likes
0
0
0

### 2 Embeds18

 http://dataminingtools.net 11 http://www.dataminingtools.net 7

### Categories

Uploaded via SlideShare as Microsoft PowerPoint

## Data Applied: ForecastPresentation Transcript

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