Leveraging external knowledge in mlapplications

Abstract

Machine learning plays an increasingly crucial role in information management in the digital age. Machine learning applications can optimize searching, classification, and data extraction processes, enabling users to access knowledge quickly. Furthermore, they support the automation of information management processes and generate value from raw data. The use of machine learning in this field enhances efficiency, accuracy, and innovation in information management, meeting diverse user needs in the digital environment. In the era of digital transformation and rapid advancements in machine learning, the ability to apply external knowledge has become a vital component in improving and expanding machine learning systems. The integration of internal expertise with external information empowers machine learning systems to make more accurate predictions, gain deeper understanding, and respond swiftly to real-world situations. This presentation focuses on researching the methods of integrating and leveraging external knowledge in machine learning applications. It examines a range of issues and applications that utilize external knowledge in the machine learning field, from image recognition to profit management and pricing strategies. The presentation also addresses emerging technology trends and potential challenges in integrating external knowledge into machine learning systems. It elucidates the overarching importance of combining internal and external knowledge for the development and enhancement of machine learning applications. By evaluating examples and real-world cases, the presentation provides valuable insights and guidance for maximizing the utilization of external knowledge in this domain