# Implementation of ELM ( Extreme Learning Machine ) algorithm for neural networks

### Description

ELM algorithm is an alternative training method for SLFN ( Single Hidden Layer Feedforward Networks ) which does not need any iterative tuning nor setting parameters such as learning rate, momentum, etc., which are current issues of the traditional gradient-based learning algorithms ( like backpropagation ).

Training of a SLFN with ELM is a three-step learning model:

Given a training set P = {(xi , ti )|xi E R , ti E R , i = 1,..., N}, hidden node
output function G(a, b, x), and the number of hidden nodes L

1) Assign randomly hidden node parameters (ai , bi ), i = 1,..., L. It means that the arc weights between the input layer and the hidden layer and the hidden layer bias are randomly generated.

2) Calculate the hidden layer output matrix H using one of the available activation functions.

3) Calculate the output weights B: B = ginv(H) %*% T ( matrix multiplication ), where T is the target output of the training set.

ginv(H) is the Moore-Penrose generalized inverse of hidden layer output matrix H. This is calculated by the MASS package function `ginv`

.

Once the SLFN has been trained, the output of a generic test set is simply Y = H %*% B ( matrix multiplication ).
Salient features:

- The learning speed of ELM is extremely fast.

- Unlike traditional gradient-based learning algorithms which only work for differentiable activation functions, ELM works for all bounded nonconstant piecewise continuous activation functions.

- Unlike traditional gradient-based learning algorithms facing several issues like local minima, improper learning rate and overfitting, etc, ELM tends to reach the solutions straightforward without such trivial issues.

- The ELM learning algorithm looks much simpler than other popular learning algorithms: neural networks and support vector machines.

### Details

Package: | elmNN |

Type: | Package |

Version: | 1.0 |

Date: | 2012-07-17 |

License: | GPL (>= 2) |

To fit a neural network, the function to use is `elmtrain`

( default version is `elmtrain.formula`

). To predict values, the function to use is `predict`

. Other functions are used internally by the training and predict functions.

### Author(s)

Alberto Gosso

Maintainer: Alberto Gosso <gosso.alberto@gmail.com>

### References

http://www.ntu.edu.sg/home/egbhuang/

G.-B. Huang, H. Zhou, X. Ding, R. Zhang (2011) *Extreme Learning Machine for Regression and Multiclass Classification* IEEE Transactions on Systems, Man, and Cybernetics - part B: Cybernetics, vol. 42, no. 2, 513-529

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

### See Also

`elmtrain.formula`

to train a neural network,`predict.elmNN`

to predict values from a trained neural network

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
set.seed(1234)
Var1 <- runif(50, 0, 100)
sqrt.data <- data.frame(Var1, Sqrt=sqrt(Var1))
model <- elmtrain(Sqrt~Var1, data=sqrt.data, nhid=10, actfun="sig")
new <- data.frame(Sqrt=0,Var1 = runif(50,0,100))
p <- predict(model,newdata=new)
Var2 <- runif(50, 0, 10)
quad.data <- data.frame(Var2, Quad=(Var2)^2)
model <- elmtrain(Quad~Var2, data=quad.data, nhid=10, actfun="sig")
new <- data.frame(Quad=0,Var2 = runif(50,0,10))
p <- predict(model,newdata=new)
``` |

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