Exploitation Strategies in Machine Learning
Exploitation strategies focus by tapping the currently world-known solutions with the aim of getting maximum benefits in the short-term.
Some common exploitation techniques in machine learning include:
- Greedy Algorithms: Greedy algorithms tend to choose the locally optimal solutions at each step without consideration of the potential impact on the overall solution. They are often efficient in terms of computation time; however, this approach may be suboptimal when sacrifices are required to achieve the best global solution
- Exploitation of Learned Policies: Reinforcement learning algorithms tend to base their pursuits on previously learned policies as a way of leveraging on old gains. This is picking the activity that amounts in high rewards, when it is similar to the previous experiences.
- Model-Based Methods: Model-based approaches take advantage of underlying models that make decisions based on their predictive capabilities.
Exploitation and Exploration in Machine Learning
Exploration and Exploitation are methods for building effective learning algorithms that can adapt and perform optimally in different environments. This article focuses on exploitation and exploration in machine learning, and it elucidates various techniques involved.
Table of Content
- Understanding Exploitation
- Exploitation Strategies in Machine Learning
- Understanding Exploration
- Exploration Strategies in Machine Learning
- Balancing Exploitation and Exploration
- Balancing Exploration and Exploitation in Multi-Armed Bandit Problem
- Problem Setup
- Strategies Incorporating Exploration and Exploitation
- Challenges and Considerations