ELM (Extreme Learning Machine)

Information

• ELM (Extreme Learning Machine) is a type of feedforward neural network that was proposed as a fast and efficient alternative to traditional backpropagation-based neural networks.
• ELM can learn from large and complex datasets with high dimensional inputs and outputs, by randomly initializing the input-to-hidden weights and analytically computing the output weights using a least-squares regression method.
• ELM can achieve comparable or even superior performance to other machine learning models, such as support vector machines (SVMs), neural networks, and decision trees, while being faster and more scalable due to its simple and parallelizable architecture.
• ELM can handle various types of data, such as continuous, discrete, categorical, and ordinal, by using appropriate activation functions and output mappings, such as sigmoid, ReLU, softmax, and linear.
• ELM can be extended to ensemble learning, where multiple ELMs with different initializations or subspaces are trained and combined to improve the generalization and robustness of the model.
• ELM has been applied to various domains, such as image classification, speech recognition, natural language processing, and financial analysis, and has shown promising results in terms of accuracy, speed, and scalability. However, its interpretability and explainability are still an open issue, as the weights are randomly initialized and may not reflect the underlying features or patterns in the data.