Learning machine beyond limits

Extreme learning machine (ELM) is an emerging artificial neural network algorithm that trains feedforward neural networks through a fast and simple method. The uniqueness of ELM is that it randomly initializes the weight matrix and bias vector of the hidden layer and only needs one forward propagation to get the output weight. This feature enables ELM to have faster training speed and better generalization performance than traditional neural networks.

The basic principle of ELM is to map the input data to a hidden layer through a randomly initialized weight matrix and bias vector, and then use a nonlinear activation function to transform the layer. Subsequently, methods such as linear regression or support vector machines are used to backpropagate the output of this layer to obtain the weight of the output layer. The weights and bias vectors of the hidden layers remain unchanged throughout the training process, which makes the training of ELM very fast and does not require complex backpropagation. ELM's random initialization method provides the basis for its unique fast training.

The main advantages of ELM include:

1. Fast training

ELM's random initialization weights and bias vectors can speed up training, and the output is usually obtained in one forward propagation Weights. Therefore, ELM is suitable for processing large-scale data sets and real-time applications.

2. Good generalization ability

The randomization process of ELM effectively avoids the over-fitting problem in traditional neural networks, thereby improving its generalization ability. In addition, ELM performs well in handling nonlinear problems and can effectively handle various types of data, including images, text, and sounds.

3. Simple and easy to use

Compared with traditional neural networks, ELM is simpler and easier to use. Only the random initialization parameters need to be set to complete the training and prediction of the network. As a result, ELM has become the first choice for many applications.

In short, ELM is a fast, simple and neural network algorithm with good generalization performance, which is widely used in various real-time applications, such as image recognition, speech recognition and natural language processing.

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