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Knowledge Engineering in ML - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Knowledge engineering in machine learning refers to the process of capturing and representing human knowledge in a form that can be utilized by computer systems. It involves designing, building, and maintaining knowledge-based systems that leverage expert knowledge to solve specific problems or make intelligent decisions. In the realm of machine learning, knowledge engineering often plays a crucial role in creating rule-based systems or expert systems. These systems rely on explicit knowledge encoded in the form of rules, heuristics, or domain-specific information to make decisions or perform tasks. The process of knowledge engineering typically includes the following steps:
Gathering relevant knowledge from human experts or existing sources.
Converting acquired knowledge into a format that a computer system can understand and utilize. This could involve defining rules, creating ontologies, or using other structured representations.
Iteratively refining the knowledge representation based on feedback and testing to improve system performance.
Combining knowledge engineering with machine learning techniques, such as supervised learning, to enhance the system's ability to generalize and adapt to new situations.
Evaluating the performance of the knowledge-based system through testing and validation against real-world scenarios.
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