# AI Technology: Understanding Supervised Learning

This article explains supervised learning, one of the most widely used approaches to machine learning.

Source: https://www.iplexlaw.co.kr/en/blog/846552

HOME / NEWS & INSIGHTS NEWS & INSIGHTS AI Technology: Understanding Supervised Learning This article explains supervised learning, one of the most widely used approaches to machine learning. AI & Software 2023.05.18 published IPLEX 4 min read This article introduces supervised learning, one of the most widely used approaches to training AI. A supervised model learns from examples with known target labels. Before training, it has not learned the task-specific relationship between inputs and those labels. Consider supervised learning through a familiar example: distinguishing dogs from cats. When first learning about animals, a child may not yet distinguish dogs from cats. An adult can teach the distinction by showing examples and naming each animal. The parents show the dog, "This animal is a dog," and they teach the cat, "This animal is a cat." . With repeated examples, the child begins to recognize the difference. The child can then apply that knowledge to other dogs and cats. This is a simple analogy for supervised learning. An untrained model has not yet learned which image features distinguish dogs from cats. To train it, prepare many dog and cat images with the correct labels. . The model uses those examples to learn patterns associated with each class. After training, it uses the learned patterns to classify new images. Let me explain each of the steps above in a more professional way. Annotation (This may differ slightly from the actual description.) A person tags an image containing a dog as 'This image is a dog image' and labels the image containing a cat as 'This image is a cat image'. The labels are usually encoded numerically, for example 0 for dog and 1 for cat. Assigning target labels to the dataset is called Annotation labeling or annotation. . Training (This may differ slightly from the actual description.) During training, Training the model's prediction is compared with the target label to compute a loss. Backpropagation computes gradients, and an optimizer updates the weights to reduce the loss. Repeating this process teaches the model to distinguish the classes. Inference (This may differ slightly from the actual description.) Using the trained model to classify a new dog or cat image is called inference . The model produces class scores or probabilities and selects a predicted class. Read the Korean source This article reflects the information available when it was published. Contact us to discuss your circumstances. Discuss this topic ↗ All articles TALK TO IPLEX Discuss your IP questions We consider your technology and business needs together. ↗ Contact us Newer IPLEX Signs an MOU with 2hrs Academy ↗ Older Lunit Patents for AI-Based Medical Image Analysis ↗ Related insights AI & Software 2026.10.01 FiX: fine-grained forgetting in softmax attention Yongduck Kim examines FiX’s feature-wise gates, numerical implementation and paged cache, distinguishing reported gains from unresolved limitations. ↗ Read article AI & Software 2026.09.30 MHAR: Reading earlier layers through different feature subspaces Yongduck Kim examines Multi-Head Attention Residuals: depth routing, reported training results, implementation costs and the relationship between technical features and effects. ↗ Read article AI & Software 2026.09.28 Column: Claude Computer Use and the Data That Trains AI Agents Writing for AI Times, IPLEX Managing Partner Yongduck Kim examines the training data behind computer-operating AI agents through U.S. Patent No. 12,585,862. ↗ Read article

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