Difference between revisions of "Learning Techniques"
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| − | <b>Core | + | <b>Core:</b> three main types of learning problems in machine learning |
* [[PRIMO.ai#Supervised|Supervised Learning]] | * [[PRIMO.ai#Supervised|Supervised Learning]] | ||
** [http://en.wikipedia.org/wiki/Deductive_classifier Deductive Inference | Wikipedia] | ** [http://en.wikipedia.org/wiki/Deductive_classifier Deductive Inference | Wikipedia] | ||
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* [[PRIMO.ai#Reinforcement Learning (RL)|Reinforcement Learning (RL)]] | * [[PRIMO.ai#Reinforcement Learning (RL)|Reinforcement Learning (RL)]] | ||
| − | <b>Hybrid | + | <b>Hybrid:</b> The lines between unsupervised and supervised learning is blurry, and there are many hybrid approaches that draw from each field of study. |
* [[PRIMO.ai#Semi-Supervised|Semi-Supervised Learning]] | * [[PRIMO.ai#Semi-Supervised|Semi-Supervised Learning]] | ||
** [http://en.wikipedia.org/wiki/Inductive_reasoning Inductive Learning | Wikipedia] | ** [http://en.wikipedia.org/wiki/Inductive_reasoning Inductive Learning | Wikipedia] | ||
Revision as of 14:10, 8 December 2019
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- 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery
- Natural Language
Core: three main types of learning problems in machine learning
Hybrid: The lines between unsupervised and supervised learning is blurry, and there are many hybrid approaches that draw from each field of study.
Other types of Learning Techniques:
- Transfer Learning a model trained on one task is re-purposed on a second related task
- Few Shot Learning
- Ensemble Learning
- Multi-Task Learning (MTL)
- Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
- Imitation Learning
- Simulated Environment Learning
- Lifelong Learning - Catastrophic Forgetting Challenge
- Active Learning
- Online Learning
