# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 2 Background
### Melm  
MELMs are an extension of the traditional extreme learning machine (ELM) algorithm that introduces additional hidden layers between the input layer and the output layer. This allows MELMs to capture more complex and abstract representations of the input data.  
Here is an overview of how MELMs work, building upon the steps described for ELMs:  
1. Input Layer: The input layer represents the features or attributes of the input data, similar to ELMs.
2. Hidden Layers: MELMs have multiple hidden layers, each consisting of randomly initialized neurons. The number of hidden layers and the number of neurons in each layer can be customized based on the complexity of the problem and the desired architecture.
3. Activation Functions: An activation function is applied to the weighted sum of inputs for each neuron in each hidden layer, introducing nonlinearity. Common activation functions used in MELMs include sigmoid, tanh, or rectified linear unit (ReLU).
4. Output Layer: The output layer produces the final predictions or classifications, using an appropriate activation function.  
Random Weight Initialization: The weights connecting each layer are randomly generated, similarly to ELMs. Each neuron in the hidden layers has its own set of weights connecting it to the previous layer.
* Calculate Hidden Layer Outputs: The random weights and the input data are multiplied, and the activation function is applied to obtain the output of each neuron in each hidden layer. The outputs from one hidden layer serve as inputs to the next hidden layer until the final hidden layer is reached.
* Output Weight Computation: The output weights connecting the last hidden layer to the output layer are computed using a linear regression technique. This involves finding the optimal values of the output weights that minimize the difference between the actual outputs and the desired outputs.
* Prediction: Once the output weights are obtained, the MELM can make predictions on new data by propagating the input through the network, computing the outputs of each hidden layer, and finally using the output weights to compute the final output.  
The introduction of multiple hidden layers in MELMs allows for the creation of a deep neural network architecture. By adding more layers, MELMs can learn hierarchical representations of the input data, capturing more complex relationships and patterns. This makes MELMs well suited for tasks that require higher levels of abstraction and representation learning.  
It is worth noting that training MELMs is similar to ELMs, where the random weights connecting the layers are initialized and the output weights are computed using linear regression. However, due to the increased complexity of the network, training deep MELMs might require more data and careful parameter tuning to avoid issues such as vanishing or exploding gradients.  
Multilayer extreme learning machines have gained attention in the field of deep learning as they provide a simpler and computationally efficient alternative to traditional backpropagation-based methods. They offer a way to leverage the benefits of deep architectures while maintaining the simplicity and fast learning speed associated with ELMs.