Project Background:
The Extreme Learning Machines (ELM) revolves around the development and application of a novel machine learning techniques. ELM is a type of artificial neural network that gained prominence in the last two decades due to its unique approach to training neural networks. Unlike traditional neural networks, ELM adopts an one-shot learning strategy where the hidden layer weights are randomly initialized, and the output layer weights are directly computed in a single step. This approach offers significant advantages in faster training times and simplicity in network architecture. ELM was initially introduced for supervised learning tasks but since found applications in various domains including classification, regression, clustering, and feature learning. The ELM research was characterized by its potential to provide efficient and effective solutions in machine learning tasks, making it a compelling alternative to conventional neural networks and traditional learning algorithms.
Problem Statement
- Enhancing the generalization performance of ELM for complex datasets with limited labeled samples.
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Developing the techniques to adapt ELM for efficient and accurate multi-class classification tasks.
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Investigating the design and training of deep ELM architectures to exploit the significance of ELM in deep learning scenarios.
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Creating online learning algorithms for ELM that can adapt to new data incrementally while maintaining model performance.
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Exploring the applications of ELM for unsupervised learning tasks, such as clustering and dimensionality reduction.
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Addressing issues of scalability and memory efficiency for handling large datasets.
Aim and Objectives
- This project involves in ELM to develop and utilize an efficient and effective machine learning technique to provide high-performance solutions for various tasks associated with traditional neural networks.
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Develop and optimize ELM algorithms to ensure fast learning and prediction, making it suitable for real-time and large-scale applications.
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Investigate techniques to enhance ELM generalization ability, allowing it to handle complex datasets and produce accurate results with limited labeled samples.
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Extend ELM to efficiently support multi-class and multi-label classification tasks.
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Explore the potential of ELM in deep learning scenarios, enabling the creation of deep ELM architectures for complex feature learning and hierarchical representations.
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Develop ELM algorithms that can adapt to new data incrementally and online, ensuring continuous model improvement.
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Enhance the interpretability of ELM models making them more understandable and transparent in critical applications.
Contributions to Extreme Learning Machines
1. Offers a faster training and prediction process compared to traditional neural networks. This efficiency is particularly valuable in real-time applications, high-dimensional feature spaces, and scenarios where computational resources are limited.
2. Efficiently handle big data and high-dimensional feature spaces, making it a valuable tool in applications involving large datasets and complex data structures.
3. This has been applied to unsupervised learning tasks, such as clustering and dimensionality reduction, providing new insights and solutions for data analysis.
4. ELM can be adapted to support multi-class and multi-label classification tasks, expanding its applicability to a wide range of classification problems.
Deep Learning Algorithms for Extreme Learning Machines
- Backpropagation
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Convolutional Neural Networks (CNN)
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Recurrent Neural Networks (RNN)
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Long Short-Term Memory (LSTM)
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Autoencoders
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Variational Autoencoders (VAE)
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Generative Adversarial Networks (GAN)
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Residual Networks (ResNets)
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Graph Neural Networks (GNN)
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Capsule Networks (CapsNets)
Datasets for Extreme Learning Machines
- MNIST
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CIFAR-10
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Fashion-MNIST
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ImageNet
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UCI Machine Learning Repository datasets
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PASCAL VOC
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COIL-20
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Caltech-101
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Caltech-256
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UCSD Anomaly Detection Dataset
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EEG Motor Movement/Imagery Dataset
Performance Metrics
- Accuracy
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Mean Squared Error (MSE)
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Root Mean Squared Error (RMSE)
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Mean Absolute Error (MAE)
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F1 Score
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Precision
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Recall
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Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
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Area Under the Precision-Recall Curve (AUC-PR)
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Cohens Kappa
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Mean Average Precision (MAP)
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Intra-Cluster Distance
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Inter-Cluster Distance
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Cross-Validation Scores
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
