Machines learn by studying data to detect patterns or by applying known rules to:
• Categorize or catalog like people or things
• Predict likely outcomes or actions based on identified patterns
• Identify hitherto unknown patterns and relationships
• Detect anomalous or unexpected behaviors
The processes machines use to learn are known as algorithms. Different algorithms learn in different ways. As new data regarding observed responses or changes to the environment are provided to the “machine” the algorithm’s performance improves. Thereby resulting in increasing “intelligence” over time.
This white paper discusses the conditions that have created the need for pattern matching techniques, the identification and creation of patterns, the Calibre Pattern Matching process, and the benefits derived from its use.