👉 Label computing, also known as supervised learning in the context of machine learning, is a process where algorithms learn to map input data (features) to specific output labels based on labeled training data. In simpler terms, it involves teaching a model to recognize patterns and assign appropriate categories or labels to new, unseen data. For instance, in image classification tasks, a model might be trained on a dataset of images labeled as "cat," "dog," or "car." Through this process, the model learns to identify visual features associated with each category and can then predict the label for new images it hasn't seen before. This approach is fundamental in various applications, including natural language processing for sentiment analysis, object detection, and speech recognition, where labeled data is crucial for training accurate and reliable models.
