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Extreme Learning Machine for land cover classification Mahesh Pal

Extreme Learning Machine for land cover classification Mahesh Pal National Institute of Technology Kurukshetra, 136119 Haryana. 1) What is a Extreme learning machine 2) Data used 3) Results and comparison with NN 4) Conclusions. EXTREME LEARNING MACHINE.

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Extreme Learning Machine for land cover classification Mahesh Pal

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  1. Extreme Learning Machine for land cover classification Mahesh Pal National Institute of Technology Kurukshetra, 136119 Haryana

  2. 1) What is a Extreme learning machine 2) Data used 3) Results and comparison with NN 4) Conclusions.

  3. EXTREME LEARNING MACHINE • Also called single hidden layer feed forward neural network (SHLFN) • Uses randomly assigned input weights and bias • Nonparametric in nature

  4. A single hidden layer feed-forward neural network is defined by (1) Where is activation function and H is the number of Hidden nodes. Where and are the weight vectors connecting inputs and the ith hidden neurons and the ith hidden neurons and output neurons respectively.

  5. is the threshold of the ith hidden neuron • is the output from single hidden layer feed forward neural network (SHLFN) for the data point j. • The SHLFN can be solved by using a gradient based solution and one need to find the suitable values of • Huang et al., (2006) suggested an alternate way to train a SHLFN by finding a smallest norm least square solution for by using Moore-Penrose generalized inverse of matrix.

  6. This solution has the following important properties (Huang et al., 2006): 1. The smallest training error can be reached by this solution. 2. Smallest norm of weights and best generalization performance. 3. The minimum norm least-square solution is a unique solution, thus involving no local minima like one in backpropagation learning algorithm.

  7. DATA USED • ETM+ ( study area UK, Littleport, Cambridgeshire, 2000) • 307-pixel (columns) by 330-pixel (rows) covering the area of interest was used.

  8. ANALYSIS • Random sampling was used to select training and test data. • Different data set is used for training and testing the classifiers • 2700 training and 2037 test pixels with 7 classes are used with ETM+ data.

  9. A standard back-propagation neural classifier (NN) with one hidden layer having 26 nodes, was used for comparison. • Classification accuracy and computational cost was used to compare both classifiers. • The performance of SHLFN depends on H (number of hidden nodes).

  10. USER DEFINED PARAMETERS • Learning rate=0.25,Momentum = 0.2, nodes in hidden layers =26, number of iterations = 2200, number of hidden layers =1, with Back propagation neural network • H =300 was found to be working well with this data using SHLFN.

  11. RESULTS

  12. COMPUTATIONAL COST

  13. CONCLUSIONS • In term of classification accuracy SHLFN work slightly better than the conventional backpropagation neural network. • Computation cost is very small while using SHLFN. • Requires one user-defined parameter.

  14. REFERENCE • Huang, G.-B. Zhu, Q.-Y. & Siew, C.-K. (2006). Extreme learning machine: Theory and applications, Neurocomputing, 70, 489–501.

  15. THANKS

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