Semi-Supervised Learning is like having a big stack of flashcards with some cards labeled and others blank. You want to learn from these flashcards to understand different categories or topics.
Imagine you’re studying for a test, and you have a mix of flashcards. Some cards already have answers on them, while others are blank. You use the labeled cards to learn the answers and then try to guess the answers for the blank ones based on what you’ve learned. Over time, you get better at guessing the answers for the blank cards because you’ve learned from the labeled ones.
Semi-Supervised Learning works in a similar way with computers. Instead of having all the data labeled with correct answers, you have a mix of labeled and unlabeled data. The computer uses the labeled data to learn how to recognize patterns and then applies this knowledge to the unlabeled data. For example, if you have a dataset with some pictures labeled as “cats” and others unlabeled, the computer learns from the labeled pictures to figure out which unlabeled ones are also cats.
This approach is useful because labeling data can be time-consuming and expensive. By using both labeled and unlabeled data, computers can still make accurate predictions without needing a lot of labeled examples.
In simple terms, Semi-Supervised Learning is about using a small amount of labeled data to help a computer learn and make sense of a larger amount of unlabeled data, improving its ability to make predictions or decisions.
