Pattern recognition in artificial intelligence refers to the ability of a system to identify patterns or regularities in data. It involves classifying input data into categories based on observed characteristics or learned experiences. The process often starts with data preprocessing, where features are extracted, followed by the identification of relevant patterns. Pattern recognition is used in various AI applications such as speech recognition, handwriting analysis, and facial recognition. Machine learning algorithms like neural networks and decision trees are often used to develop pattern recognition models. For example, in facial recognition, the system learns to identify unique features of a person's face, such as the distance between eyes, nose shape, and other distinguishing characteristics. When a new image is presented, the system matches these features with stored patterns to identify or verify the person. Pattern recognition can be applied in industries like healthcare, where it’s used to recognize patterns in medical images for disease diagnosis, and in finance for fraud detection by recognizing irregular patterns in transaction data.
What is pattern recognition in artificial intelligence (AI)?
- Exploring Vector Database Use Cases
- Advanced Techniques in Vector Database Management
- GenAI Ecosystem
- Evaluating Your RAG Applications: Methods and Metrics
- How to Pick the Right Vector Database for Your Use Case
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How do multi-agent systems balance workloads?
Multi-agent systems balance workloads by distributing tasks among multiple agents based on their capabilities, availabil
How does positional tracking contribute to immersion in VR?
Positional tracking is crucial for creating an immersive experience in virtual reality (VR) because it allows the system
Why are two sentences that are paraphrases of each other not receiving a high similarity score with my Sentence Transformer model?
Two paraphrased sentences might not receive a high similarity score from a Sentence Transformer model due to differences