How active learning can train machine learning models with less data

  • Random sampling: the data points are sampled at random.
  • Uncertainty sampling: Points are selected based on the ML model’s prediction uncertainty of their class.
  • Entropy sampling: Points are selected with maximal class probability entropy.
  • Margin sampling: Points are chosen for whom the difference between the most and second most likely classes are the smallest.
  • The probabilities in these strategies are associated with the predictions of the SVM classifier.

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Founded by Professors Stephen Roberts and Michael Osborne, pioneers in the field of AI and Machine Learning, the mission of Mind Foundry is to create a future where AI and Humans work together to solve the world’s most important problems. And we’re hiring!

Author

Dr. Alessandra Tosi is a leading researcher in Bayesian Optimisation and probabilistic machine learning at Mind Foundry, where she has been leading a range of government funded projects in the sector of Energy and Sustainability. Over her extensive career, her work has focused on pushing the boundaries of AI through the integration of software solutions and original research. She has a particular interest in exploring the important ethical and moral dilemmas that are emerging as the role of artificial intelligence in our world expands.

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Mind Foundry

Mind Foundry

Artificial Intelligence for high-stakes applications.