1. Genetic Algorithms

• Core Concept: Inspired by natural selection, these algorithms evolve a population of solutions through processes like mutation and crossover.
• Uniqueness: Exploits the power of evolutionary principles for optimization in complex, non-linear spaces.
• Applications: Feature selection, neural network architecture search, financial portfolio optimization.

2. One-Class SVM

• Core Concept: Unlike traditional SVMs that separate two classes, this algorithm defines the boundary around a single class.
• Uniqueness: Ideal for anomaly detection, novelty detection, and identifying outliers in data.
• Applications: Fraud detection, anomaly detection in sensor data.

3. Isotonic Regression

• Core Concept: Enforces monotonicity constraints on the regression function, ensuring the output either increases or decreases with the input.
• Uniqueness: Valuable when the underlying relationship between variables is expected to exhibit a specific monotonic trend.
• Applications: Calibration of probabilistic models, ranking systems, dose-response analysis.

4. Gaussian Processes

• Core Concept: Provides a probabilistic framework for regression and classification, modeling relationships as continuous functions with uncertainty quantification.
• Uniqueness: Offers a principled way to handle uncertainty and model complex relationships.
• Applications: Bayesian optimization, spatial statistics, time series analysis.

5. Neuroevolution

• Core Concept: Combines evolutionary algorithms with neural networks to optimize their structure and weights.
• Uniqueness: Overcomes limitations of traditional gradient-based methods for training neural networks, especially in complex or dynamic environments.
• Applications: Reinforcement learning, evolving specialized neural networks for specific tasks.

Note: This list is not exhaustive, and the field of machine learning constantly evolves with new and innovative algorithms.

Article contributed by : Sachin