Semi-supervised learning is a type of machine learning where the training data is not fully labeled. This can be a difficult problem to solve because the data is not complete and may not be consistent. There are a few different ways to approach this problem, and the best way depends on the data and the problem that is being solved.
One way to solve the semi-supervised learning problem is by using a generative model. A generative model is a model that can generate new data that is similar to the training data. This is a good approach to take if the training data is not too large and is relatively simple. Another approach is to use a discriminative model. A discriminative model is a model that predicts labels for data. This approach is better when the training data is larger and more complex.
Neural networks can be used for both generative and discriminative models. For a generative model, the neural network takes in input data and generates new data that is similar to the input data. For a discriminative model, the neural network takes in input data and predicts labels for the data. There are many different types of neural networks, and the best type to use depends on the data and the problem that is being solved.
Machine learning can also be used to solve the semi-supervised learning problem. There are many different types of machine learning algorithms, and the best type to use depends on the data and the problem that is being solved. Some popular machine learning algorithms include support vector machines, decision trees, and k-nearest neighbors.
Large language models (LLMs) can also be used to solve the semi-supervised learning problem. LLMs are models that are trained on large amounts of text data. They can be used to generate new text data or to predict labels for text data. LLMs are often used in natural language processing tasks, such as sentiment analysis and named entity recognition.
There are many different ways to solve the semi-supervised learning problem. The best approach depends on the data and the problem that is being solved. AI, neural networks, machine learning, and large language models are all tools that can be used to solve this problem.
References:
https://en.wikipedia.org/wiki/Semi-supervised_learning
https://towardsdatascience.com/gentle-introduction-to-semi-supervised-learning-a267bfabda72
https://machinelearningmastery.com/gentle-introduction-to-the-bootstrap-method/
https://www.stat.berkeley.edu/~surya/pubs/nips_workshop09.pdf
https://www.quora.com/What-is-the-best-way-to-solve-a-semi-supervised-learning-problem
https://towardsdatascience.com/machine-learning-algorithms-part-9-k-nearest-neighbors-algorithm-601813cd0b0a
https://towardsdatascience.com/support-vector-machines-svm-c9ef22815588
https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052
https://nlp.stanford.edu/projects/glove/
https://ai.googleblog.com/2016/06/regularizing-and-optimizing-lstm.html